A Beginners Guide and Tutorial for GANs   
Via O'Reilly, supported by Google. A Simplified Tutorial.  Instructive, not the idea of a network generating content.

Beginner's guide to GANs

This tutorial will show you how to build a generative adversarial network that learns to generate handwritten digits—essentially you'll teach a neural network how to write. You can download and modify the code from this tutorial on GitHub.

Generative Adversarial Networks for Beginners
Build a neural network that learns to generate handwritten digits.
By Jon Bruner,  Adit Deshpande  
Practical Generative Adversarial Networks for Beginners

You can download and modify the code from this tutorial on GitHub ... 

According to Yann LeCun, “adversarial training is the coolest thing since sliced bread.” Sliced bread certainly never created this much excitement within the deep learning community. Generative adversarial networks—or GANs, for short—have dramatically sharpened the possibility of AI-generated content, and have drawn active research efforts since they were first described by Ian Goodfellow et al. in 2014.

GANs are neural networks that learn to create synthetic data similar to some known input data. For instance, researchers have generated convincing images from photographs of everything from bedrooms to album covers, and they display a remarkable ability to reflect higher-order semantic logic.   .... " 


          Generative Neural Networks and Marketing   
A good explanation of generative neural networks,  how they work and their applications.  There has been some conversation about their implications for video use and marketing.  Any real examples out there?   Exploring.


          Modelling design and degradation using artificial neural network   
Lin, Hungyen and Kong, L. X. and Hsu, Hung-Yao (2005) Modelling design and degradation using artificial neural network. In: Intelligent Systems and Robotics. Conference and International Advanced Technology. Congress. University of Putra, Malaysia, pp. 670-682.
          Comment on Peering into neural networks by Alvaro   
We need to see the positive aspects of a.i. i think it Will be our best way to beat diseases and other challenges.
           Design and Analysis of a General Recurrent Neural Network Model for Time-Varying Matrix Inversion    
Zhang, Yunong and Ge, Shuzhi Sam (2005) Design and Analysis of a General Recurrent Neural Network Model for Time-Varying Matrix Inversion. IEEE Transactions on Neural Networks, 16 (6). pp. 1477-1490. ISSN 1045-9227
           Fast Robust Subject-Independent Magnetoencephalographic Source Localization Using an Artificial Neural Network    
Jun, Sung Chan and Pearlmutter, Barak A. (2005) Fast Robust Subject-Independent Magnetoencephalographic Source Localization Using an Artificial Neural Network. Human Brain Mapping, 24 (1). pp. 21-34.
          Assessment of PD severity in gas-insulated switchgear with an SSAE   
Scientific partial discharge (PD) severity evaluation is highly important to the safe operation of gas-insulated switchgear. However, describing PD severity with only a few statistical features such as discharge time and discharge amplitude is unreliable. Hence, a deep-learning neural network model called stacked sparse auto-encoder (SSAE) is proposed to realise feature extraction from the middle layer with a small number of nodes. The output feature that is almost similar to the input PD information is produced in the model. The features extracted from PD data are then fed into a soft-max classifier to be classified into one of four defined PD severity states. In addition, unsupervised greedy layer-wise pre-training and supervised fine-tuning are utilised to train the SSAE network during evaluation. Results of testing and simulation analysis show that the features extracted by the SSAE model effectively characterise PD severity. The performance of the SSAE model, which possesses an average assessment accuracy of up to 92.2%, is better than that of the support vector machine algorithm based on statistical features. According to the tested number of SSAE layers and features and the training sample size, the SSAE model possesses good expansibility and can be useful in practical applications.
          Article: Introduction to Neural Networks   
Introduction to Neural Networks http://flip.it/R8U5xG
          I love you for the day   

'I love you for the day' is an installation created by Matthias Maurer & Guillaume Massol situated at the unlikely intersection between mass surveillance and poetry.

The installation is constantly looking around, focusing from time to time on passers-by. Once focused on a person it’s tracking his/her face and breaks it down into fragments, generating a constantly evolving mosaic of eyes, mouthes and noses. As it watches, it generates lyrics and music based on facial features. While on idle mode it reload bits of previously tracked faces and keeps on generating soundscapes and lyrics.

Text and music are created by a multi-layer Recurrent Neural Network. A model trained on love songs generates the text of the installation character by character, while another model – trained on heavy metal – generates the melody which is slowed down and played with electronic instruments.

The soundtrack of the video as well as the name of the installation has been generated by a RNN as well.

Cast: Guillaume

Tags: LED, machine learning, generative, installation, ai and generative music


           Face detection using artificial neural network approach    
Nazeer, Shahrin Azuan and Omar, Nazaruddin and Jumari, Khairol Faisal and Khalid, Marzuki (2007) Face detection using artificial neural network approach. First Asia International Conference On Modeling & Simulation (AMS 2007) . pp. 394-399.
          Foxy Well Politicians   

The inevitable happened last night - Foxy came for his dinner only to run into Kitty. There was a bit of a scuffle and Kitty saw him off. As soon as Kitty came off guard duty he came back though.

Hay had an appointment yesterday at something called a Well Woman Clinic. Bloody oxymoron, if you ask me. A bloke doesn't even go to see his GP when he's ill, never mind about when he's well.


Apparently a team of scientists have used algebraic topology, a branch of mathematics used to describe the properties of objects and spaces regardless of how they change shape, to analyse the brain. They found that groups of neurons connect into 'cliques', and that the number of neurons in a clique would lead to its size as a high-dimensional geometric object. "We found a world that we had never imagined," says lead researcher, neuroscientist Henry Markram from the EPFL institute in Switzerland. "There are tens of millions of these objects even in a small speck of the brain, up through seven dimensions. In some networks, we even found structures with up to 11 dimensions." 

Human brains are estimated to have a staggering 86 billion neurons, with multiple connections from each cell webbing in every possible direction, forming the vast cellular network that somehow makes us capable of thought and consciousness. With such a huge number of connections to work with, it's no wonder we still don't have a thorough understanding of how the brain's neural network operates.

In order to simplify the study, I'm led to believe that the team will be working on Brexit supporting Conservative politicians' brains. They'll move on to something more complex at a later stage.



          For AI startups, more funding is often not the answer   

One of the hottest areas for VC investment at the moment is AI/machine learning — that includes artificial intelligence algorithms, related machine learning systems, neural networks, and back-end processing to produce insightful and self-learning applications. As Nvidia’s CEO recently said: Software may be eating the world, but AI is going to eat software AI investment […]


          Artificial Synapses Could Lead to Smarter AI   
By replicating the function of the human brain's 100 trillion synapses, scientists hope to boost the versatility of artificial neural networks.

via Live Science http://ift.tt/2usMSal
          #23 Volvo admite que sus autos de conducción automática son confundidos por los canguros (EN)   

No hay nada que una Convolutional Neural Network no pueda solucionar.

» autor: rolrex


          (Summary) A Fusion Face Recognition Approach based on 7-Layer Deep Learning Neural Network   
Title : A Fusion Face Recognition Approach based on 7-Layer Deep Learning Neural Network Author : Jianzheng Liu, Chunlin Fang, and ChaoWu Research Object : Face Recognition Why Face Why Face Recognition Available methods and Advantages and Disadvantages Lu et al Method (Discriminative Multi-Manifold Analysis/DMMA) (+) Training is just only one sample face image. Most of Face […]
          MIT CSAIL research offers a fully automated way to peer inside neural nets   

MIT CSAIL research offers a fully automated way to peer inside neural netsMIT's Computer Science and Artificial Intelligence Lab has devised a way to look inside neural networks and shed some light on how they're actually making decisions. The new process is a fully automated version of the system the research team behind it presented two years ago, which employed human reviewers to achieve the same ends.Coming up...



          Combined thresholding and neural network approach for vein pattern extraction from leaf images   
Living plant recognition based on images of leaf, flower and fruit is a very challenging task in the field of pattern recognition and computer vision. There has been little work reported on flower and fruit image processing and recognition. In recent years, several researchers have dedicated their work to leaf characterisation. As an inherent trait, leaf vein definitely contains the important information for plant species recognition despite its complex modality. A new approach that combines a thresholding method and an artificial neural network (ANN) classifier is proposed to extract leaf veins. A preliminary segmentation based on the intensity histogram of leaf images is first carried out to coarsely determine vein regions. This is followed by a fine segmentation using a trained ANN classifier with ten features extracted from a window centred on the object pixel as its inputs. Compared with other methods, experimental results show that this combined approach is capable of extracting more accurate venation modality of the leaf for the subsequent vein pattern classification. The approach can also reduce the computing time compared with a direct neural network approach
          Customer Data Target Specialist - 000000187202 munkakörbe keresünk munkatársat. | Feladatok: Cr...   
Customer Data Target Specialist - 000000187202 munkakörbe keresünk munkatársat. | Feladatok: Create propensity calculations by analyzing the account landscape, product and account features and other data sources • Find prospects for new and existing Vodafone products based on the power of predictive analytics • Deliver in-depth and detailed custom reports for Sales and Marketing managers • Continuously develop, maintain and update VGE?s customer targeting tool and marketing dashboard • Respond to any data and insight related queries • Hold analytics related and marketing enablement trainings • Assist in the development of different custom analytics dashboards • Write macros in VBA to automate processes • Provide support in creating algorithms in R. | Mit ajánlunk: Possibility to work from home once a week • We provide working assets as laptop and mobile phone with Vodafone RED subscription • Internal coaching/mentoring culture • Internal career opportunities • Expert program | Elvárások: Fluent English knowledge • 2+ years experience in BI/ Reporting/Finance/SFDC Support/Corporate/SSC environment • Advanced Excel knowledge, intermediate VBA knowledge • Have at least a basic understanding of Machine Learning kNN, Bayesian Probability, Artificial Neural Network • Advantage: R and Python knowledge is preferred otherwise either Octave, Matlab, SAS or SPSS • Experience working with Salesforce especially reporting or equivalent • Strong analytical mindset, attention to details • Good communication skills and stakeholder management | További infó és jelentkezés itt: www.profession.hu/allas/1041201
          For AI startups, more funding is often not the answer   

One of the hottest areas for VC investment at the moment is AI/machine learning — that includes artificial intelligence algorithms, related machine learning systems, neural networks, and back-end processing to produce insightful and self-learning applications. As Nvidia’s CEO recently said: Software may be eating the world, but AI is going to eat software AI investment […]


          Industry Outlook on Global Neural Network Software Market 2017-2021   
?Report: Global Neural Network Software Market 2017-2021 is a new market research publication announced by Reportstack. Report Outline: Neural network software is used in researching, stimulating, developing, and applying artificial neural networks (ANN) to several arrays of adaptive systems such as artificial intelligence (AI) and machine learning. Neural network simulators are software applications used in simulating the behavior of artificial or biological neural networks. For...
          Nuit Blanche in Review (June 2017)   

Since the last Nuit Blanche in Review (May 2017) we've had three implementations related to Deep Neural Networks, a few in-depth post ranging from training nets to compressive sensing,  a dataset, two Paris Machine Learning meetups, one meeting announcement, several videos of talks and four job announcements. Enjoy !

Implementations

In depth

Book
Dataset

Paris Machine Learning meetup

Meeting
slides

Videos

Job:






Join the CompressiveSensing subreddit or the Google+ Community or the Facebook page and post there !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.

          Deep Learning / Computer Vision Research Engineer   
NY-New York City, If you are a Deep Learning / Computer Vision Engineer with experience, please read on! What You Will Be Doing More info coming soon What You Need for this Position At Least 3 Years of experience and knowledge of: - Deep Learning - Computer Vision - Image Processing - Object Recognition - Convolutional Neural Networks - CNNs So, if you are a Deep Learning / Computer Vision Engineer with experience,
          Deep Learning / Computer Vision Research Engineer   
NY-New York City, If you are a Deep Learning / Computer Vision Engineer with experience, please read on! What You Will Be Doing More info coming soon What You Need for this Position At Least 3 Years of experience and knowledge of: - Deep Learning - Computer Vision - Image Processing - Object Recognition - Convolutional Neural Networks - CNNs So, if you are a Deep Learning / Computer Vision Engineer with experience,
          Deep Learning / Computer Vision Research Engineer   
New York, If you are a Deep Learning / Computer Vision Engineer with experience, please read on! What You Will Be Doing More info coming soon What You Need for this Position At Least 3 Years of experience and knowledge of: - Deep Learning - Computer Vision - Image Processing - Object Recognition - Convolutional Neural Networks - CNNs So, if you are a Deep Learning / Computer Vision Engineer with experience,
          Microsoft squeezed AI onto a Raspberry Pi   
“The dominant paradigm is that these [sensor] devices are dumb,” said senior researcher with Microsoft Research India, Manik Varma. Now, Varma’s team in India and Microsoft researchers in Redmond, Washington, (the entire project is led by lead researcher Ofer Dekel) have figured out how to compress neural networks, the synapses of Machine Learning, down from […]
          Nuit Blanche in Review (June 2017)   

Since the last Nuit Blanche in Review (May 2017) we've had three implementations related to Deep Neural Networks, a few in-depth post ranging from training nets to compressive sensing,  a dataset, two Paris Machine Learning meetups, one meeting announcement, several videos of talks and four job announcements. Enjoy !

Implementations

In depth

Book
Dataset

Paris Machine Learning meetup

Meeting
slides

Videos

Job:






Join the CompressiveSensing subreddit or the Google+ Community or the Facebook page and post there !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.

          Comment on Text Generation With LSTM Recurrent Neural Networks in Python with Keras by Jason Brownlee   
The raw data are sequences of integers. There is only one observation (feature) per time step and it is an integer. That is why the first reshape specifies one feature.
          Comment on Text Generation With LSTM Recurrent Neural Networks in Python with Keras by Kunal chakraborty   
Hello Jason, Great tutorial. I have just one doubt though, in np.reshape command what does feature mean? and why is it set to 1?
          Comment on Regression Tutorial with the Keras Deep Learning Library in Python by Roy   
Hi, Thank you for the tutorial. Few questions here. 1. What is the differences when we use KerasRegressor(build_fn=baseline_model, nb_epoch=100, batch_size=5, verbose=0) and with model.fit(x_train, y_train, batch_size=batch_size,epochs=epochs,verbose=1,validation_data=(x_test, y_test))? AFAIK, with when using KerasRegressor, we can do CV while can't on model.fit. Am I right? Will both result in the same MSE etc? 2. How do create a neural network that predict two continuous output using Keras? Here, we only predict one output, how about two or more output? How do we implement that? (Multioutput regression problem?)
          Performance Assessment and Prediction for Superheterodyne Receivers Based on Mahalanobis Distance and Time Sequence Analysis   
The superheterodyne receiver is a typical device widely used in electronics and information systems. Thus effective performance assessment and prediction for superheterodyne receiver are necessary for its preventative maintenance. A scheme of performance assessment and prediction based on Mahalanobis distance and time sequence analysis is proposed in this paper. First, a state observer based on radial basis function (RBF) neural network is designed to monitor the superheterodyne receiver and generate the estimated output. The residual error can be calculated by the actual and estimated output. Second, time-domain features of the residual error are then extracted; after that, the Mahalanobis distance measurement is utilized to obtain the health confidence value which represents the performance assessment result of the most recent state. Furthermore, an Elman neural network based time sequence analysis approach is adopted to forecast the future performance of the superheterodyne receiver system. The results of simulation experiments demonstrate the robustness and effectiveness of the proposed performance assessment and prediction method.
          Integration of individual and social information for decision-making in groups of different sizes   
by Seongmin A. Park, Sidney Goïame, David A. O’Connor, Jean-Claude Dreher When making judgments in a group, individuals often revise their initial beliefs about the best judgment to make given what others believe. Despite the ubiquity of this phenomenon, we know little about how the brain updates beliefs when integrating personal judgments (individual information) with … Continua la lettura di Integration of individual and social information for decision-making in groups of different sizes
          Sequential sampling of visual objects during sustained attention   
by Jianrong Jia, Ling Liu, Fang Fang, Huan Luo In a crowded visual scene, attention must be distributed efficiently and flexibly over time and space to accommodate different contexts. It is well established that selective attention enhances the corresponding neural responses, presumably implying that attention would persistently dwell on the task-relevant item. Meanwhile, recent studies, … Continua la lettura di Sequential sampling of visual objects during sustained attention
          Optimal structure of metaplasticity for adaptive learning   
by Peyman Khorsand, Alireza Soltani Learning from reward feedback in a changing environment requires a high degree of adaptability, yet the precise estimation of reward information demands slow updates. In the framework of estimating reward probability, here we investigated how this tradeoff between adaptability and precision can be mitigated via metaplasticity, i.e. synaptic changes that … Continua la lettura di Optimal structure of metaplasticity for adaptive learning
          Two different mechanisms support selective attention at different phases of training   
by Sirawaj Itthipuripat, Kexin Cha, Anna Byers, John T. Serences Selective attention supports the prioritized processing of relevant sensory information to facilitate goal-directed behavior. Studies in human subjects demonstrate that attentional gain of cortical responses can sufficiently account for attention-related improvements in behavior. On the other hand, studies using highly trained nonhuman primates suggest that … Continua la lettura di Two different mechanisms support selective attention at different phases of training
          Technology Associate, Team Lead for ATS Principal Based Strategies - Morgan Stanley - New York, NY   
Machine Learning including Regression and Clustering techniques, Support Vector Machines, Neural Networks, Probabilistic Graphical Models, and Econometric;...
From Morgan Stanley - Mon, 22 May 2017 20:45:35 GMT - View all New York, NY jobs
          Pixhawk 2 and Jetson TX1?   

This sounds very interesting. I'm currently using a Raspberry Pi 3 communicating with the Pixhawk using Dronekit and opencv for feature recognition. I want to move towards the use of neural networks and consider the Pi somewhat limited for this purpose, despite its great flexibility.

The Jetson is the natural choice, but I'm not sure what software combination to use. Dronekit or ROS? Caffe or Tensorflow?

All thoughts welcome.


          Comment on Geometry of linearized neural networks by 192.168.O.1   
Thanks for the great post, Seb! Regarding the canonical controllable form question in the post, I learned it from this lecture notes
          Comment on AlphaGo is born by 192.168 10.1   
There is a neural network for learning the value function, but also a neural network as a policy - it is trained both by imitation (on expert games) and by self-play.
           پروژه پردازش تصوير با متلب،داده کاوي،شبکه هاي کامپيوتري،فازي،شبيه سازي،سمينار،ش    
انجام کليه پروژه هاي دانشجويي درسراسرايران تحت تمامي زبانهاي برنامه نويسي انجام پايان نامه و پروپوزال هاي دانشجويي مقاطع تحصيلي کارداني کارشناسي کارشناسي ارشددكترا و.... دانشگاه هاي داخل و خارج از کشوررشته کامپيوترو فناوري اطلاعات و.. خدمات مشاوره اي: مشاوره رايگان انتخاب موضوع پايان نامه - انجام تمامي خدمات مربوط به تهيه پيشنهاديه پايان نامه ( proposal ) مشاوره و تدوين پايان نامه هاي مرتبط با رشته هاي فوق الذکرفناوري اطلاعات* کامپيوترو.. ارائه تمامي فصول مربوط به پايان نامه ، مطابق با جدول زمانبندي توافقي مشاوره و طراحي پرسش نامه و انجام مصاحبه و تجزيه و تحليل اطلاعات استخراجي با استفاده ازنرم افزارهاي مرتبط و در انتها ارائه مقاله اي علمي – پژوهشي براي ارائه نشريات معتبر داخلي (علمي-پژوهشي) و خارجي (ISI)IEEE*نگارش، تدوين و اديت مقاله هاي isi براي ارسال به ژورنال هاي معتبر با ايمپکت فاکتور بالا رشته فناوري اطلاعات * گرايش تجارت* الکترونيک -*کارشناسي ارشد SIMULINK, cloud storager و IMAGE PROCESSING و GENETIC ALGORITHM و NEURAL NETWORK* و FUZZY LOGIC Steganalysis Facial expression Face recognition Texture segmentation Image retrieval Image segmentation Color Demosaicing ... Machine Vision: Object tracking( with all kind of methods) for various purposes Multiple Object Tracking Object Tracking with motion blur Blind motion blur deconvolution line based structure from motion Geometrical enhancemen *webrecommendation پروژه هاي محيط سيمولينک (Simulink) پروژه هاي بازشناسي الگو (pattern recognition) پروژه هاي کدنويسي مختلف و پروژه هاي مرتبط با جعبه ابزارهاي: Aerospace• neural network*• symbolic math*• comminucation*• bioinformatic*• curve fitting*• control system*• econometric• database*• datafeed*• filter design*• image acqusition*• signal processing*• optimization* انجام پروژه هاي برنامه نويسي دانشجوئي براي دروس دانشگاهي : ==================================== * مباني کامپيوتر * برنامه سازي پيشرفته * سيستم هاي تجاري * ساختمان داده * طراحي الگوريتم * ذخيره و بازيابي اطلاعات * نظريه زبانها و ماشين ها * هوش مصنوعي * کامپايلر * ريزپردازنده,vhdl,z80,… IVR ، 8051 * شبکه هاي کامپيوتري * گرافيک کامپيوتري * مهندسي نرم افزار * پايگاه داده *كارآفريني *كارآموزي *مباحث ويژه *معماري کامپيوتر * سيستم عامل پيشرفته *يادگيري ماشين *پردازش موازي *روش تحقيق *سمينار *پردازش سيگنال *پردازش صوت *شبيه سازي وبهينه سازي * آزمايشگاه هاي (سيستم عامل ، ريزپردازنده ، مدار منطقي ، پايگاه داده) ليست زبانهاي برنامه نويسي تخصصي ما به شرح زير مي باشد: ==================================== VB.Net2005,2008.,2010 C#.Net2005,2008.,2010 ASP.Net2005,2008.,2010 ++C C VB - Visual Basic 6.0 Pascal DELPHI Visual C++ Database: SQL Server Access php Html Java J2EE J2me Assembly Matlab برنامه نويسي موبايل NET. تحت (Pocket PC) XML, AJAX, Java Script) Oracle Ns2 Opnet ……, گروه مهندسي پايتخت - انجام پروژه هاي دانشجويي شما با قيمتي مناسب پذيرش سفارش پروژه داخل وخارج ازکشور هرگونه کپي برداري ازآگهي غيرمجازمي باشد جهت سفارش پروژه تماس بگيريد ازديگرپروژهاي ماديدن فرماييد www.pcporoje.com http://tezcomputer.com http://tezcomputercom.blogfa.com مهندس خسروي 09191022908 جهت سفارش پروژه يا نياز به هرگونه اطلاع رساني فقط با ايميل زير با مادر تماس باشيد infoporoje.net@gmail.com
           موضوعات پايان نامه کارشناسي ارشد هوش مصنوعي،نرم افزار،شبکه کاميوتري    
انجام کليه پروژه هاي دانشجوييدرسراسرايران بيش از 20 پروژه برنامه نويسيوپايان نامه پروپوزال هاي دانشجويي از دپارتمان علوم رايانه دانشگاه هاي کلمبيا هندمالزي آلمان*سوئد*دانمارک *انگلستان *فيليپين *دبي*ترکيه و... دربانک پروژه پايتخت توسط خودگروه نرم افزاري پايتخت انجام پروژه هاي دانشجويي براي دانشجويانايراني داخل وخارجازکشوررشته کامپيوتر Several suggested student programming projects for computer science majors (undergraduate, masters and PhD. students) from the Network Security Lab at Columbi@انجام پايان نامه وپروپوزال هاي دانشجويي مقاطع تحصيلي کارداني کارشناسي کارشناسي ارشددكترا و....دانشگاه هاي داخل و خارج از کشوررشته کامپيوترنرم افزار*معماري کامپيوتر*هوش مصنوعي و فناوري اطلاعات و.........امنيت شبکه* مخابرات امن *تجارت الکترونيک تحت تمامي زبانها برنامه نويسي خدمات مشاوره اي: مشاوره رايگان انتخاب موضوع پايان نامه - انجام تمامي خدمات مربوط به تهيه پيشنهاديه پايان نامه ( proposal ) مشاوره و تدوين پايان نامه هاي مرتبط با رشته هاي فوقالذکرفناوري اطلاعات* کامپيوترو.. ارائه تمامي فصول مربوط به پايان نامه ، مطابق با جدول زمانبنديتوافقي مشاوره و طراحي پرسش نامه و انجام مصاحبه و تجزيه و تحليل اطلاعات استخراجي با استفاده ازنرم افزارهاي مرتبط و در انتها ارائه مقاله اي علمي – پژوهشيبراي ارائه نشريات معتبر داخلي (علمي-پژوهشي) و خارجي (ISI)IEEE*نگارش، تدوين و اديت مقاله هاي isi براي ارسال به ژورنال هاي معتبر با ايمپکت فاکتور بالا رشته فناوري اطلاعات * گرايش تجارت* الکترونيک -*کارشناسي ارشد درمورد نقش erp و سيستم هاي اطلاعاتي و ريسک در هوش تجاري بررسي انواع چالش‌هاي موجود در رايانش ابري و رايانش توري(Cloud computing amp; Grid computing) شامل مباحث امنيت (Security)، ذخيره‌سازي (Storage)، کارايي (Performance)، دسترس‌پذيري (Availability) و مديريت، تخصيص و زمانبندي منابع (Allocation and Scheduling Resources)، توازن بار(Load Balancing). بررسي انواع الگوريتم‌ها در حوزه‌ي داده‌کاوي (Data Mining)؛ طبقه‌بندي(Classification)، خوشه‌بندي(Clustering)، کشف قوانين انجمني(Association Rules)، پيش‌بيني سري‌زماني(Time Series Prediction)، انتخاب ويژگي (Feature Selection) و استخراج ويژگي (Feature Extraction)، کاهش بعد(Dimensionality Reduction)، شخصي سازي نتايج موتورهاي جستجو و داده‌کاوي اطلاعات آنها(Search Engine). بررسي انواع الگوريتم‌ها در حوزه‌ي شبکه‌هاي اجتماعي(Social Network)؛ کشف ساختار(structure Detection ) کشف اجتماعات(Community Detection)، تشخيص اسپم(Spam Filter). بررسي انواع تکنولوژي‌هاي ذخيره داده اي، Sql، NoSql، نگاشت کاهش (MapReduce)، هادوپ(Hadoop)، کار با Big Data. بررسي، مقايسه و بهبود انواع الگوريتم‌هاي مکاشفه‌اي، فرا مکاشفه‌اي و چند هدفه مانند الگوريتم ژنتيک(Genetic Algorithm, MOGA, NSGAII)، الگوريتم ازدحام ذرات(PSO, MOPSO)، الگوريتم مورچگان(Ant Colony)، الگوريتم زنبور عسل(Bee clolony)، الگوريتم رقابت استعماري(ICA)، الگوريتم فرهنگي (Cultural Algorithm)، الگوريتم تکامل تفاضلي(DE). بررسي انواع الگوريتم‌هاي پردازش تصوير(IMAGE PROCESSING)؛ تشخيص چهره(Face Recognation)، قطعه‌بندي تصاوير(Image Segmentation)، فشرده‌سازي تصاوير(Image Compression)، نهان‌نگاري تصاوير(Watermarking). بررسي انواع الگوريتم‌هاي يادگير؛ شبکه‌هاي عصبي (ANFIS, ANN)، شبکه‌هاي بيزين(Bayesian Network)، ماشين بردار پشتيبان(SVM). استفاده از نرم‌افزار‌هاي Visual Studio، متلب(Matlab)، وکا(Weka)، رپيدماينر(Rapidminer)، Clementine، کلودسيم(Cloudsim). استفاده از زبان‌هاي Python, Java, C, C#, C++, DBMS, MySql, Sql Server, VB.NET, PHP تدوين پروپوزال، اجراي پايان نامه و طرح هاي پژوهشي و … وبررسي الگوريتمهاي شبکهاي گيريد* داده کاوي (Data Mrining) در زمينه هاي دسته بندي (Classification)، خوشه بندي (Clustering)، پيش بيني (Prediction)، انتخاب ويژگي (Feature Selection) و قواعدانجمني (Association Rules) با*وب سرويس و....الگوريتمlulea*سيستم هاي چندعامله ژنتيك* شبكه عصبي *هوش مصنوعي * شبيه سازي *بهينه سازي *سمينار*–الگوريتم چندهدفه* تكاملي *سيمولينک*بينايي ماشين*فازيکامينز*. Image Processing amp; Machine vision* SIMULINK, cloud storagerو IMAGE PROCESSING و GENETIC ALGORITHM و NEURAL NETWORK*و FUZZY LOGIC Steganalysis Facial expression Face recognition Texture segmentation Image retrieval Image segmentation Color Demosaicing ... Machine Vision: Object tracking( with all kind of methods) for various purposes Multiple Object Tracking Object Tracking with motion blur Blind motion blur deconvolution line based structure from motion Geometrical enhancemen *webrecommendation پروژه هاي محيط سيمولينک (Simulink) پروژه هاي بازشناسي الگو (pattern recognition) پروژه هاي کدنويسي مختلف و پروژه هاي مرتبط با جعبه ابزارهاي: • Aerospace• neural network*• symbolic math*• comminucation*• bioinformatic*• curve fitting*• control system*• econometric• database*• datafeed*• filter design*• image acqusition*• signal processing*• optimization* انجام پروژه هاي حاوي پايگاه داده و پروژه هاي گرافيکي تحت تمامي زبان هاي برنامه نويسي 1 - شبکه هاي عصبي مصنوعي چند لايه پرسپترون2 - شبکه هاي عصبي مصنوعي با تابع پايه شعاعي3 - درختان تصميم گيري طبقه بندي و رگرسيوني4 - مدل هاي درختي5 - ماشين هاي بردار حامي طبقه بندي و رگرسيوني5 - ماشين هاي بردار حامي طبقه بندي و رگرسيوني6 - سيستم هاي استنباط فازي7 - سيستم هاي استنباط فازي - عصبي8 - سيستم استنباط بيزين با استفاده از نرم افزارهاي: Clementine, SPSS, WEKA, Rapid Miner, Qnet, انجام پروژهاي برنامه نويسي دلفي ، جاوا ، ويژوال بيسيك ،وي بي دانت .وي بي 6*مطلب- پي اچ پي , ، اكسس ، سي شارپ اي اس پي *پارلوگ *پرولوگ *سي *سي پلاس پلاس *مولتيمديابيلدرو....*رديابي *مکانيابي *sar* الگوريتم تطبيقي يادگيري براي رتبه بندي : با رويکرد آتاماتاي يادگير * شبکه هاي MANET براي کاربردهاي چند رسانه اي* يادگيري تقويتي براي تقسيم بار پردازشي در شبکه توزيع شده با معماري گيريد* وسايل نقليه اي با قابليت شناسايي حملات Dos *بدافزاردرشبکه عصبي *بدافزارها وشناسايي آنها*c-means*Fuzzy k-means معماري سرويس گزا*داده گرا/*soaسيسستمهاي تشخيص نفوذ*کامپيوتري هاي بيومولکولي *سيگنال هاي الكتريكي بيو مـولـكـولي مرتب سازي شبکه Sorting-Network انجام پروژه هاي تلفن گويا ، برنامه هاي ارتباطي ، پاسخگوي خودکار ، سيستم پيغام گير و برنامه نويسي تحت شبکه پروژهاي شبکه حسگرو... دارنده بزرگترين بانک سورس هاي آماده به تمامي زبانهاي برنامه نويسي ( انجام شده توسط خود گروه ) پايتخت مقاله هاي جديدومعتبرباشبيه سازي *2015*2014*2013*2012*2011*2010 پروژه خودرامتخصانشان ارائه دهيدنه به موسسات انجام پروژه چون هم نمي دانند شما چه مي خواهيدوهم هزينه براي خوددريافت مي کنند درست وبا اطمينان انتخاب کنيد همراه مستندات و توضيحات کامل ، و خط به خط دستورات و نيز نحوه ساخت و چگونگي اجراي پروژه ها، بهمراه دايکيومنت (Document) تايپ شده و آماده براي صحافي بهمراه پشتيباني بعد از تحويل پروژه بعد ازتحقيق بررسي ازچند مورد تماس با ما درمورد کلاه برداري با استفاده ازاسم گروه پايتخت تحقيق وبررسي ما آغازگرديدپس ازجستجو دراينترنت متوجه شديم اشخاصي ديگري با استفاده نام اعتبارگروه نرم افزاري پايتخت اقدام به کلاه برداري و سوه استفاده ازطريق آگهي هاي همانندآگهي هاي گروه پايتخت نموده اند بدين وسيله گروه نرم افزاري پايتخت اعلام مي داردکه اين اشخاص به هيچ عنوان جزوه گروه ما نمي باشندوتنها تلفن پاسخ گو ازطريق گروه نرم افزاري پايتخت به شماره 09191022908مهندس خسروي مي باشد www.pcporoje.com 09191022908 خسروي گروه نرم افزاري پايتخت هيچ گونه مسئوليتي را جهت بي دقتي کاربران وسوه استفاده هاي احتمالي ازآنها نمي پذيرد انجام پروژه هاي برنامه نويسي دانشجوئي براي دروس دانشگاهي : * مباني کامپيوتر * برنامه سازي پيشرفته * سيستم هاي تجاري * ساختمان داده * طراحي الگوريتم * ذخيره و بازيابي اطلاعات * نظريه زبانها و ماشين ها * هوش مصنوعي * کامپايلر * ريزپردازنده,vhdl,z80,…IVR ، 8051 * شبکه هاي کامپيوتري * گرافيک کامپيوتري * مهندسي نرم افزار * پايگاه داده *كارآفريني *كارآموزي *مباحث ويژه *معماري کامپيوتر * سيستم عاملپيشرفته *يادگيري ماشين *پردازش موازي *روش تحقيق *سمينار *پردازش سيگنال *پردازش صوت *شبيه سازي وبهينه سازي * آزمايشگاه هاي (سيستم عامل ، ريزپردازنده ، مدار منطقي ، پايگاه داده) ليست زبانهاي برنامه نويسي تخصصي ما به شرح زير مي باشد: Database: SQLServer Access php Html Java J2EE J2me Assembly Matlab برنامه نويسيموبايل NET. تحت (Pocket PC) XML, AJAX, JavaScript) Oracle Ns2 Opnet ……, همراه :09191022908 خسروي ليست پروژه هاي آماده تحت تمامي زبانهاي برنامه نويسي سيستم آرشيو اطلاعات پروژه هاي دانشجويي سفارش پروزه ازدانشگاه انگلستان يک نانوايي مي خواهد سيستم توزيع خودش را بهينه کند سفارش پروژه ازدانشگاه انگلستان نرم افزارارسال اس ام اس وايميل سفارش پروزه ازدانشگاه ترکيه شبيه سازي ميل سرورياهو سفارش پروزه ازدانشگاه آلمان سيستم ام ارپي سفارش پروزه ازدانشگاه هند فروشگاه اينترنتي سفارش پروزه ازدانشگاه مالزي کتابخانه صوتي براي لينوکس سفارش پروزه ازدانشگاه مجارستان پياده سازي همکار به همکار شبکه سفارش پروژه ازدانشگاه دبي الگوريتم fcfs سفارش پروژه ازدانشگاه فيليپين دانلودرايگان پروژه هاي دانشجويي دارنده بزرگترين بانک سورس هاي آماده به تمامي زبانهاي برنامه نويسي ( انجام شده توسط خود گروه ) پايتخت درضمن برخي ازاين پروژهاهم تحت ويندوزدرآرشيوموجوداست وهم تحت وب برنامه اسباب بازي فروشي*حملات سياه چالانه AODV *مقاله هاي جديد ومعتبرباشبيه سازي 2015*2014*20113*2012*2011 *ارسال مقاله وسمينار*نويزگيري تصوير* کاربردسيستمهايچندعاملهدريادگيريالکترونيک*وب معنايي وابزارهاي ان * تشخيص چهره روي تصوير و ويديو*حذف اثر حرکت از روي تصاوير*تخمين قدرت سيگنال در شبکه مخابراتي بي سيم و تعيين مکان بهينه براي فزستنده ها *بررسي و شبيه سازي مدل سينوسي سيگنال صحبت * بررسي و مقايسه سيستمهاي عامل بلادرنگ*بررسي پروتکل SRM در شبيه ساز NS-2*بررسي روشهاي کد کردن بردارهاي جابجايي در فشرده سازي سيگنالهاي ويد يويي*طراحي و ساخت اجزاء تکرار کننده GSM*پياده سازي کدينگ کانال Reed-Solomon بر روي سيگنال ويديو بي سيم*شناسايي چهره انسان در تصاوير رنگي*نهان نگاري تصاوير ديجيتال در حوزه ويولت* سيستمهاي ارسال ديجيتال صوت*جداسازي سيگنالهاي صوتي مخلوط شده به روش BSS* مطالعه و بررسي امضاء هاي ديجيتال*بررسي و شبيه سازي چيدمان بهينه ادوات شبکه هاي بدون سيم*بررسي الگوريتمهاي نهان نگاري تصوير و پياده سازي آنها*سيستم اتاق عمل *بررسي روشهاي مختلف حذف نويز در سيگنالهاي ديجيتال*تحليل روشهاي فضا- زمان در سيستمهاي مخابرات بي سيم*نهان نگاري صوتي*نهان نگاري تصاوير ديجيتال با استفاده از تبديل موجک *روشهاي تکراري براي جبران اعوجاج ناشي از درونيابي *MAC جهت دار در شبکه هاي بي سيم ad hoc * Taxonomy and Survey of Cloud Computing Systemscloud *storager*محاسبات ابري opnetشبيه سازي شبکه با استفاده از WIP** روشهاي حفاظت از اطلاعات در فرآيند انتقال و دريافت مقايسه بانك هاي اطلاعاتي اسكيوال واوراكل * امنيت ATM- پايگاه داده توزيع شده سيستم مرسولات پستي اداره پست به کمک معماري سرويس گرا و تکنيک model_driven engineering شبيه سازي ns2 *تشخيص چهره انسان به روش تحليل تفکيکي خطي دو بعدي( 2D-LDA به همراه مقاله *تشخيص حرکت از طريق ورودي دوربين يا وبکم* تشخيص کارکتر و عدد در تصوير OCR* تشخيص عدد فارسي در تصوير (به همراه آموزش فارسي)* تشخيص حروف فارسي در تصوير به روش تطبيق الگو* تشخيص حروف فارسي در تصوير به روش شبکه عصبي* شبيه سازي مدولاسيون پالسهاي كدشده PCM* شبيه سازي و بررسي انواع اتصال کوتاه در ژنراتور* شبيه سازي ورقه کردن فلز* شبيه سازي بازوي ربات (به همراه مقاله)* ترميم تصوير Image *طراحي مدارهاي *ابرکامپيوترها*داده هاي با حجم بسياربالا inpainting* ترميم ويدئو Video inpainting** برنامه تشخيص بارکد (پردازش تصوير) اتحاديهخريدكارمندانوخريدكالاهايمشابهبهافراد*بررسي مکانيزم احرازهويت *fcfs*الگوريتم کاهش نويز در تصويرNoise Canceling*بررسي کليه توابع توزيع در متلبDistributions functions* پياده سازي روش گوشه شمال غربي *North-West Corner Method* برنامه تبديل اتوماتيک کد فرترن به متلب بهينه سازي تنش در تراس *پنهان‌نگاريتصاوير يا Steganography با متلب*• بدست آوردن پروفايل دما در سطح مقطع steak در زمان هاي مختلف بعد از قرار گرفتن در ظرف روغن شبيه سازي راکتور batch (ناپيوسته) و رسم نمودار غلظت ها* يكسوساز سه فاز تريستوري با *پروژه يادگيري ماشين يا تشخيص جنسيت زن مرد *machine learning**• تشخيص لبه تصوير توسط الگوريتم کلوني مورچه ها ACO (به همراه مقاله) پردازشتصويرWavelet بهبود مدل کاربر در وب¬سايت بصورت خودکار با استفاده ازمعناشناسي با مفاهيم خاص دامنه*پروژه هاي مهندسي معكوس *طراحي سايت b2b تشخيص هويت افراد با استفاد شناساي كف دست *نظرسنجي *الگوريتم پنتيک چندهدفه * • محاسبه جريان درون لوله و عدد رينولدز به کمک روابط سوامي و جين و دارسي-ويسباخ • شبيه سازي کنترل مقاوم عصب* تحليگرلغوي*چندضلعي *جدول متقاطع * فرستادن ايميل *شبيه سازي پروتکل مسيريابي شبکه حسگر بي سيم باآپ نت پروژه هاي تشخيص هويت :عنبه *اثرانگشت *تشخيص چهره به چهره *كف دست * الگوريتم هاي خوشه بندي در شبکه هاي حسگر موبايلعنوان* امضاي ديجيتال**امنيت اطلاعات * بررسي امنيت شبکه در مقوله پدافند غير عامل * بيومتريک (Biometric)*الگوريتم زنبورعسل *دنباله کاوي *شناسايي خط *شناسايي صورت *بينايي ماشين*هوش مصنوعي دربازي *وب معنايي*آنتولوژي *فشرده سازي تصوير*پردازش صوت * امنيت درپايگاه توزيع شده*فايل هاي ويرانگر - - - سيستم فروش و صورتحساب- سيستم حضورغياب با اثر انگشت - سيستم صندوق رستوراني و فروشگاهي با سخت افزار و نرم افزار POS گروه مهندسي پايتخت - انجام پروژه هاي دانشجويي شما با قيمتي مناسب پذيرش سفارش پروژه داخل وخارج ازکشور هرگونه کپي برداري ازآگهي غيرمجازمي باشد جهت سفارش پروژه تماس بگيريد ازديگرپروژهاي ماديدبفرماييد www.pcporoje.com http://tezcomputer.com http://tezcomputercom.blogfa.com مهندس خسروي 09191022908 جهت سفارش پروژه يا نياز به هرگونه اطلاع رساني فقط با ايميل زير با مادر تماس باشيد infoporoje.net@gmail.com
          Deep Learning / Computer Vision Research Engineer   
New York, If you are a Deep Learning / Computer Vision Engineer with experience, please read on! What You Will Be Doing More info coming soon What You Need for this Position At Least 3 Years of experience and knowledge of: - Deep Learning - Computer Vision - Image Processing - Object Recognition - Convolutional Neural Networks - CNNs So, if you are a Deep Learning / Computer Vision Engineer with experience,
          Attention in Long Short-Term Memory Recurrent Neural Networks   

The Encoder-Decoder architecture is popular because it has demonstrated state-of-the-art results across a range of domains. A limitation of the architecture is that it encodes the input sequence to a fixed length internal representation. This imposes limits on the length of input sequences that can be reasonably learned and results in worse performance for very […]

The post Attention in Long Short-Term Memory Recurrent Neural Networks appeared first on Machine Learning Mastery.


          Graph Convolutional Networks for Molecules. (arXiv:1706.09916v1 [cs.LG])   

Authors: Zhenpeng Zhou

Representation learning for molecules is important for molecular properties prediction, material design, drug screening, etc. In this work a graph convolutional network architecture for learning representations for molecules is presented. An operation for convolving k-neighbourhood of a specific node in graph is defined, which is corresponding to kernel size of k in convolutional neural networks. Besides, A module of adaptive filtering is defined to find the sampling locations based on graph connections and node features.


          Automated Audio Captioning with Recurrent Neural Networks. (arXiv:1706.10006v1 [cs.SD])   

Authors: Konstantinos Drossos, Sharath Adavanne, Tuomas Virtanen

We present the first approach to automated audio captioning. We employ an encoder-decoder scheme with an alignment model in between. The input to the encoder is a sequence of log mel-band energies calculated from an audio file, while the output is a sequence of words, i.e. a caption. The encoder is a multi-layered, bi-directional gated recurrent unit (GRU) and the decoder a multi-layered GRU with a classification layer connected to the last GRU of the decoder. The classification layer and the alignment model are fully connected layers with shared weights between timesteps. The proposed method is evaluated using data drawn from a commercial sound effects library, ProSound Effects. The resulting captions were rated through metrics utilized in machine translation and image captioning fields. Results from metrics show that the proposed method can predict words appearing in the original caption, but not always correctly ordered.


          A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem. (arXiv:1706.10059v1 [cs.AI])   

Authors: Zhengyao Jiang, Dixing Xu, Jinjun Liang

Financial portfolio management is the process of constant redistribution of a fund into different financial products. This paper presents a financial-model-free Reinforcement Learning framework to provide a deep machine learning solution to the portfolio management problem. The framework consists of the Ensemble of Identical Independent Evaluators (EIIE) topology, a Portfolio-Vector Memory (PVM), an Online Stochastic Batch Learning (OSBL) scheme, and a fully exploiting and explicit reward function. This framework is realized in three instants in this work with a Convolutional Neural Network (CNN), a basic Recurrent Neural Network (RNN), and a Long Short-Term Memory (LSTM). They are, along with a number of recently reviewed or published portfolio-selection strategies, examined in three back-test experiments with a trading period of 30 minutes in a cryptocurrency market. Cryptocurrencies are electronic and decentralized alternatives to government-issued money, with Bitcoin as the best-known example of a cryptocurrency. All three instances of the framework monopolize the top three positions in all experiments, outdistancing other compared trading algorithms. Although with a high commission rate of 0.25% in the backtests, the framework is able to achieve at least 4-fold returns in 30 days.


          Superpixel-based semantic segmentation trained by statistical process control. (arXiv:1706.10071v1 [cs.CV])   

Authors: Hyojin Park, Jisoo Jeong, Youngjoon Yoo, Nojun Kwak

Semantic segmentation, like other fields of computer vision, has seen a remarkable performance advance by the use of deep convolution neural networks. Many recent studies on this field upsample smaller feature maps into the original size of the image to label each pixel into one of semantic categories. However, considering that neighboring pixels are heavily dependent on each other, both learning and testing of these methods have a lot of redundant operations. To resolve this problem, the proposed network is trained and tested with only 0.37\% of total pixels by superpixel-based sampling and largely reduced the complexity of upsampling calculation. In doing so, scale and translation invariant features are constructed by making the hypercolumns using the feature maps obtained by pyramid module as well as the feature maps in convolution layers of the base network. Since the proposed method uses a very small number of sampled pixels, the end-to-end learning of the entire network is difficult with a common learning rate for all the layers, which is caused by the small sampling ratio. In order to compensate for this, the learning rate after sampling is controlled by statistical process control (SPC) of gradients in each layer. The proposed method performs better than or equal to the conventional methods that use much more samples on Pascal Context, SUN-RGBD dataset.


          Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning. (arXiv:1706.10207v1 [stat.ML])   

Authors: Frank E. Curtis, Katya Scheinberg

The goal of this tutorial is to introduce key models, algorithms, and open questions related to the use of optimization methods for solving problems arising in machine learning. It is written with an INFORMS audience in mind, specifically those readers who are familiar with the basics of optimization algorithms, but less familiar with machine learning. We begin by deriving a formulation of a supervised learning problem and show how it leads to various optimization problems, depending on the context and underlying assumptions. We then discuss some of the distinctive features of these optimization problems, focusing on the examples of logistic regression and the training of deep neural networks. The latter half of the tutorial focuses on optimization algorithms, first for convex logistic regression, for which we discuss the use of first-order methods, the stochastic gradient method, variance reducing stochastic methods, and second-order methods. Finally, we discuss how these approaches can be employed to the training of deep neural networks, emphasizing the difficulties that arise from the complex, nonconvex structure of these models.


          Improving Session Recommendation with Recurrent Neural Networks by Exploiting Dwell Time. (arXiv:1706.10231v1 [cs.IR])   

Authors: Alexander Dallmann (1), Alexander Grimm (1), Christian Pölitz (1), Daniel Zoller (1), Andreas Hotho (1 and 2) ((1) University of Würzburg, (2) L3S Research Center)

Recently, Recurrent Neural Networks (RNNs) have been applied to the task of session-based recommendation. These approaches use RNNs to predict the next item in a user session based on the previ- ously visited items. While some approaches consider additional item properties, we argue that item dwell time can be used as an implicit measure of user interest to improve session-based item recommen- dations. We propose an extension to existing RNN approaches that captures user dwell time in addition to the visited items and show that recommendation performance can be improved. Additionally, we investigate the usefulness of a single validation split for model selection in the case of minor improvements and find that in our case the best model is not selected and a fold-like study with different validation sets is necessary to ensure the selection of the best model.


          Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes. (arXiv:1706.10239v1 [cs.LG])   

Authors: Lei Wu, Zhanxing Zhu, Weinan E

It is widely observed that deep learning models with learned parameters generalize well, even with much more model parameters than the number of training samples. We systematically investigate the underlying reasons why deep neural networks often generalize well, and reveal the difference between the minima (with the same training error) that generalize well and those they don't. We show that it is the characteristics the landscape of the loss function that explains the good generalization capability. For the landscape of loss function for deep networks, the volume of basin of attraction of good minima dominates over that of poor minima, which guarantees optimization methods with random initialization to converge to good minima. We theoretically justify our findings through analyzing 2-layer neural networks; and show that the low-complexity solutions have a small norm of Hessian matrix with respect to model parameters. For deeper networks, extensive numerical evidence helps to support our arguments.


          Bridging the Gap between Probabilistic and Deterministic Models: A Simulation Study on a Variational Bayes Predictive Coding Recurrent Neural Network Model. (arXiv:1706.10240v1 [cs.AI])   

Authors: Ahmadreza Ahmadi, Jun Tani

The current paper proposes a novel variational Bayes predictive coding RNN model, which can learn to generate fluctuated temporal patterns from exemplars. The model learns to maximize the lower bound of the weighted sum of the regularization and reconstruction error terms. We examined how this weighting can affect development of different types of information processing while learning fluctuated temporal patterns. Simulation results show that strong weighting of the reconstruction term causes the development of deterministic chaos for imitating the randomness observed in target sequences, while strong weighting of the regularization term causes the development of stochastic dynamics imitating probabilistic processes observed in targets. Moreover, results indicate that the most generalized learning emerges between these two extremes. The paper concludes with implications in terms of the underlying neuronal mechanisms for autism spectrum disorder and for free action.


          A selectional auto-encoder approach for document image binarization. (arXiv:1706.10241v1 [cs.CV])   

Authors: Jorge Calvo-Zaragoza, Antonio-Javier Gallego

Binarization plays a key role in the automatic information retrieval from document images. This process is usually performed in the first stages of documents analysis systems, and serves as a basis for subsequent steps. Hence it has to be robust in order to allow the full analysis workflow to be successful. Several methods for document image binarization have been proposed so far, most of which are based on hand-crafted image processing strategies. Recently, Convolutional Neural Networks have shown an amazing performance in many disparate duties related to computer vision. In this paper we discuss the use of a convolutional auto-encoder devoted to learning an end-to-end map from an input image of a fixed size to its selectional output, in which activations indicate whether the pixel must be classified as foreground or background. Once trained, documents can therefore be binarized by parsing them through the model and applying a threshold. Our approach has proven to outperform state-of-the-art techniques in the well-known DIBCO dataset (edition 2016).


          SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud. (arXiv:1706.10268v1 [cs.LG])   

Authors: Zahra Ghodsi, Tianyu Gu, Siddharth Garg

Inference using deep neural networks is often outsourced to the cloud since it is a computationally demanding task. However, this raises a fundamental issue of trust. How can a client be sure that the cloud has performed inference correctly? A lazy cloud provider might use a simpler but less accurate model to reduce its own computational load, or worse, maliciously modify the inference results sent to the client. We propose SafetyNets, a framework that enables an untrusted server (the cloud) to provide a client with a short mathematical proof of the correctness of inference tasks that they perform on behalf of the client. Specifically, SafetyNets develops and implements a specialized interactive proof (IP) protocol for verifiable execution of a class of deep neural networks, i.e., those that can be represented as arithmetic circuits. Our empirical results on three- and four-layer deep neural networks demonstrate the run-time costs of SafetyNets for both the client and server are low. SafetyNets detects any incorrect computations of the neural network by the untrusted server with high probability, while achieving state-of-the-art accuracy on the MNIST digit recognition (99.4%) and TIMIT speech recognition tasks (75.22%).


          An artificial neural network to find correlation patterns in an arbitrary number of variables. (arXiv:1606.06564v2 [cs.LG] UPDATED)   

Authors: Alessandro Fontana

Methods to find correlation among variables are of interest to many disciplines, including statistics, machine learning, (big) data mining and neurosciences. Parameters that measure correlation between two variables are of limited utility when used with multiple variables. In this work, I propose a simple criterion to measure correlation among an arbitrary number of variables, based on a data set. The central idea is to i) design a function of the variables that can take different forms depending on a set of parameters, ii) calculate the difference between a statistics associated to the function computed on the data set and the same statistics computed on a randomised version of the data set, called "scrambled" data set, and iii) optimise the parameters to maximise this difference. Many such functions can be organised in layers, which can in turn be stacked one on top of the other, forming a neural network. The function parameters are searched with an enhanced genetic algortihm called POET and the resulting method is tested on a cancer gene data set. The method may have potential implications for some issues that affect the field of neural networks, such as overfitting, the need to process huge amounts of data for training and the presence of "adversarial examples".


          Online and Linear-Time Attention by Enforcing Monotonic Alignments. (arXiv:1704.00784v2 [cs.LG] UPDATED)   

Authors: Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck

Recurrent neural network models with an attention mechanism have proven to be extremely effective on a wide variety of sequence-to-sequence problems. However, the fact that soft attention mechanisms perform a pass over the entire input sequence when producing each element in the output sequence precludes their use in online settings and results in a quadratic time complexity. Based on the insight that the alignment between input and output sequence elements is monotonic in many problems of interest, we propose an end-to-end differentiable method for learning monotonic alignments which, at test time, enables computing attention online and in linear time. We validate our approach on sentence summarization, machine translation, and online speech recognition problems and achieve results competitive with existing sequence-to-sequence models.


          Stable Architectures for Deep Neural Networks. (arXiv:1705.03341v2 [cs.LG] UPDATED)   

Authors: Eldad Haber, Lars Ruthotto

Deep neural networks have become invaluable tools for supervised machine learning, e.g., classification of text or images. While often offering superior results over traditional techniques and successfully expressing complicated patterns in data, deep architectures are known to be challenging to design and train such that they generalize well to new data. Important issues with deep architectures are numerical instabilities in derivative-based learning algorithms commonly called exploding or vanishing gradients. In this paper we propose new forward propagation techniques inspired by systems of Ordinary Differential Equations (ODE) that overcome this challenge and lead to well-posed learning problems for arbitrarily deep networks.

The backbone of our approach is our interpretation of deep learning as a parameter estimation problem of nonlinear dynamical systems. Given this formulation, we analyze stability and well-posedness of deep learning and use this new understanding to develop new network architectures. We relate the exploding and vanishing gradient phenomenon to the stability of the discrete ODE and present several strategies for stabilizing deep learning for very deep networks. While our new architectures restrict the solution space, several numerical experiments show their competitiveness with state-of-the-art networks.


          Attention Is All You Need. (arXiv:1706.03762v4 [cs.CL] UPDATED)   

Authors: Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.0 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.


          Comment on Text Generation With LSTM Recurrent Neural Networks in Python with Keras by Jason Brownlee   
The raw data are sequences of integers. There is only one observation (feature) per time step and it is an integer. That is why the first reshape specifies one feature.
          Comment on Text Generation With LSTM Recurrent Neural Networks in Python with Keras by Kunal chakraborty   
Hello Jason, Great tutorial. I have just one doubt though, in np.reshape command what does feature mean? and why is it set to 1?
          Codementor: Classifying Traffic Signs   
Using TensorFlow I train a Convolutional Neural Network to classify traffic signs with 95% accuracy.
          Guy Haworth   
...
Jan Krabbenbos, Jaap van den Herik, Guy Haworth (2015). The World Computer Speed Chess Championship 2015. ICGA Journal, Vol. 38, No. 2
Jan Krabbenbos, Jaap van den Herik, Guy Haworth (2017). WCCC 2016: The 22nd World Computer Chess Championship. ICGA Journal, Vol. 39, No. 1 » WCCC 2016
Jan Krabbenbos, Jaap van den Herik, Guy Haworth (2017). 7th World Chess Software Championship. pdf » WCSC 2017
Forum Posts
Neural Networks in Chess by Guy Haworth, CCC, June 23, 2000 » Neural Networks

          Self-teaching neural networks help find mysterious stars tearing through the Milky Way (VIDEO)   
Preview Astronomers at the European Space Agency (ESA) combined an “artificial brain” with observations from the advanced Gaia space satellite to discover six stars darting across our galaxy following as-yet-poorly-understood encounters with Milky Way’s supermassive black hole.
Read Full Article at RT.com
          Commenti su Vivo – nono racconto di malosmannaja   
altro racconto molto bello e dagli intensi risvolti drammatici, dove il disagio del protagonista nasce - a mio modo di sentire - più dal padre alcoolista e dal trauma lombosacrale con conseguente paraplegia che dalla tecnologia in sé. però la cosa non disturba, nel senso che in fondo Blue Whale è specchio abbastanza fedele della società e del disagio sociale giovanile, quindi la tecnologia, pur non innescandolo, diventa efficace strumento di amplificazione e trasmissione del disagio/contagio. peraltro, tornando su quanto scritto in precedenti commenti, non so se in realtà ormai la distinzione tra soggetto e oggetto, tra agente e subente, tra fine e strumento conservi un qualche valore: viviamo in una *crealtà* molto più complicata dei segnali che ogni porzione di essa ci invia, dove la realtà oggettiva è meno tangibile della “realtà fiat”, ovvero della realtà virtuale creata dal nulla mediante condivisione in rete (magari si tratta di un mio delirio, ma mi pare di ricordare che qualche filosofo abbia scritto “la realtà è solo un’allucinazione più condivisa delle altre”, o qualcosa del genere). ecco dunque che se il *neural network* del singolo essere umano è integrato/riplasmato dai *social network* diventa sempre più difficile capire chi agisca cosa o cosa agisca chi… eh, in pratica, alla domanda in stampatello posta dall’autore (più che da Luca) “i sogni possono consumare la mente?”, credo si potrebbe rispondere che la mente, comedicelaparola, ovviamente mente e che pertanto basta ascoltare l’eco di una parola astratta per scoprirsi intenti a sognare. : ) del racconto in oggetto, poi, nello specifico mi è piaciuta in modo particolare l’intensità emotiva che non arretra di fronte alla malattia e al “vuoto, gelido e onnipresente che avvolge ogni cosa”. Luca è un piccolo grande eroe proprio perché vive la sua fragilità (l’esatto opposto del trionfante “sei un vero uomo” intriso di retorica che gli propina il “creatore del gioco”) e ho trovato coraggiosissimo e originale il fatto che il racconto non si concluda con uno scontato salto nel vuoto. tra le cose che invece mi sono piaciute di meno, sicuramente alcune intrusioni della voce dell’autore nel corpo testo (ad esempio le due domande, quella in stampatello citata più sopra, e l’altra “Una domanda, per un attimo, affiora nella mente turbata e confusa di Luca: che razza di società è quella in cui un ragazzo per sentirsi vivo deve morire?) e alcune scelte che mi sono sembrate un po’ troppo teatrali alla fine del brano. ad esempio il messaggio in bottiglia lo eliminerei proprio, o lo “girerei in soggettiva” nel senso che Luca vede la bottiglia e *immagina* che dentro ci sia un biglietto scritto da suo padre, desidera così tanto che il messaggio ci sia che raccoglie la bottiglia e quasi lo sente tra le dita, lo srotola ma è tutto bianco. altra cosa che mi lascia sempre un po’ perplesso sono le urla inconsulte e disperate in cima a un palazzo (fanno molto film americano), ma magari è solo questione di gusti e di esperienze (gli urlatori li ho visti solo al cinema). da ultimo nota particolare in chiusa per quel “giocare”, potentissimo e ricondotto alla sua natura più umana e godibilmente infantile (non a caso i bambini), che torna in antitesi al “gioco totale” (Blue Whale), restituendo Luca alla vita.
          A Neural Network Approach to Discrete Choice Modeling   
【作者(必填)】Jiann-Min Jeng, Daniel R Fesenmaier 【文题(必填)】A Neural Network Approach to Discrete Choice Modeling 【年份(必填)】1996 【全文链接或数据库名称(选填)】
          Based on Soft Competition ART Neural Network Ensemble and Its Application to the Fault Diagnosis of Bearing   
This paper presents a novel method for fault diagnosis based on an improved adaptive resonance theory (ART) neural network and ensemble technique. The method consists of three stages. Firstly, the improved ART neural network is comprised of the soft competition technique based on fuzzy competitive learning (FCL) and ART based on Yu’s norm, the neural nodes in the competition layer are trained according to the degree of membership between the mode node and the input, and then fault samples are classified in turn. Secondly, with the distance evaluation technique, the optimal features are obtained from the statistical characteristics of original signals and wavelet coefficients. Finally, the optimal features are input into the neural network ensemble (NNE) based on voting method to identify the different fault categories. The proposed method is applied to the fault diagnosis of rolling element bearings, and testing results show that the neural network ensemble can reliably classify different fault categories and the degree of faults, which has a better classification performance compared with the single neural network.
          Deep neural networks for direct, featureless learning through observation: the case of 2d spin models   
We train a deep convolutional neural network to accurately predict the energies and magnetizations of Ising model configurations, using both the traditional nearest-neighbour Hamiltonian, as well as a long-range screened Coulomb Hamiltonian. We demonstrate the capability of a convolutional deep neural network in predicting the nearest-neighbour energy of the 4×4 Ising model. Using its success […]
          (USA-WA-Seattle) Applied Scientist   
Applied Scientist Job ID 551723 Location US-WA-Seattle Posted Date 6/28/2017 Company Amazon Corporate LLC Position Category Machine Learning Science Recruiting Team .. Job DescriptionSeeking Applied Researchers to build the future of the Alexa Shopping Experience at Amazon. At Alexa Shopping, we strive to enable shopping in everyday life. We allow customers to instantly order whatever they need, by simply interacting with their smart devices such as Echo, Fire TV, and beyond. Our services allow you to shop, anywhere, easily without interrupting what you're doing - to go from "I want" to "It's on the way" in a matter of seconds. We are seeking the industry's best applied scientists to help us create new ways to shop. Join us, and help invent the future of everyday life. The products you would envision and craft require ambitious thinking and a tireless focus on inventing solution to solve customer problems. You must be passionate about creating algorithms and models that can scale to hundreds of millions of customers, and insanely curious about building new technology and unlocking its potential. The Alexa Shopping team is seeking an Applied Scientist who will partner with technology and business leaders to build new state-of-the-art algorithms, models and services that surprise and delight our voice customers. As part of the new Alexa Shopping team you will use ML techniques such as deep learning to create and put into production models that deliver personalized shopping recommendations, allow to answer customer questions and enable human-like dialogs with our devices. Basic QualificationsThe ideal candidate will have a PhD in Mathematics, Statistics, Machine Learning, Economics, or a related quantitative field, and 5+ years of relevant work experience, including: * Proven track record of achievements in natural language processing, search and personalization. * Expertize on a broad set of ML approaches and techniques, ranging from Artificial Neural Networks to Bayesian Non-Parametrics methods. * Experience in Structured Prediction and Dimensionality Reduction. * Strong fundamentals in problem solving, algorithm design and complexity analysis. * Proficiency in at least one scripting languages (e.g. Python) and one large-scale data processing platform (e.g. Hadoop, Hive, Spark). * Experience with using could technologies (e.g. S3, Dynamo DB, Elastic Search) and experience in data warehousing. * Strong personal interest in learning, researching, and creating new technologies with high commercial impact. Preferred Qualifications * Track record of peer reviewed academic publications. * Strong verbal/written communication skills, including an ability to effectively collaborate with both research and technical teams and earn the trust of senior stakeholders. Amazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation
          Technology Associate, Team Lead for ATS Principal Based Strategies - Morgan Stanley - New York, NY   
Machine Learning including Regression and Clustering techniques, Support Vector Machines, Neural Networks, Probabilistic Graphical Models, and Econometric;...
From Morgan Stanley - Mon, 22 May 2017 20:45:35 GMT - View all New York, NY jobs
          Last Week on Channel 9: June 26th - July 2nd, 2017   

Lots of great stuff last week. While below is a select 34 videos, shows, episodes and posts, the big news is a cool new Channel 9 feature, Channel 9: subscribe to show feature!

TWC9: .NET Core 2 (and Friends) Preview 2, .future, Imagine Cup 2017, “Adventures of Ninja…

This week on Channel 9, [Host 1] and [Host 2] discuss the week's top developer news, including;

Highlight Reel - 2017 Imagine Cup US Finals

Tune in here to watch the exclusive behind-the-scenes highlights from the 2017 US Imagine Cup Finals, Microsoft's premier student tech competition. Check out the innovative and cutting edge demos. Take a sneak peek at exclusive storytelling and startup tips then watch students prep for their big moment and take the stage!  Want to learn more? Watch all the in-depth videos showcasing the entire event!

Let's talk about Power BI Premium - Adam and Patrick Unplugged - June 2017 [EP4]

In this episode of the Adam and Patrick Unplugged show, we talk about some travel updates, ragged hierarchies and a lot about Power BI Premium.

Episode 231: Azure Functions Local Debugging and More with Donna Malayeri

In this episode Thiago Almeida and Bill Barnes are joined by Donna Malayeri, a Program Manager in the Azure Functions team. Donna joins us to show local debugging of Azure Functions written in JavaScript and C# Class Libraries as well as other features and news. ...

Innovation at Microsoft: An Overview of Microsoft Research

Analysts visit Microsoft AI +R to learn about research approach and demos of projects.

Data Predictive Control: Bridging Machine Learning and Controls for Volatile Energy Markets

In January 2014, the east coast (PJM) electricity grid experienced an increase in the price of electricity from $31/MWh to $2,680/MWh in a matter of 10 minutes. This extreme price volatility has become the new norm in our electric grids. Building additional peak generation capacity is not environmentally or economically sustainable. Furthermore, the traditional view of Energy Efficiency does not address this need for Energy Flexibility. The solution lies with Demand Response (DR) from the customer side - curtailing demand during peak capacity for financial incentives. However, this is a very hard problem for commercial, industrial and institutional plants, the largest electricity consumers: they cannot model each building as they are all unique, cannot decide which of the 100,000's of control knobs to turn as it is too complex, must rely on rule-based curtailment approaches which are ad hoc, inefficient and do not provide any guarantees for energy reduction. ...

Channel 9: subscribe to show feature!

Channel 9 viewers and content creators have been asking for this feature for so long. You asked--we answered!

Now you can subscribe to your favorite shows on Channel 9. Now I know what you're thinking, "That sounds too good to be true. My favorite shows straight to my inbox?!" Believe it. POOF! You're welcome....

Introducing Azure DB for PostgreSQL

Saloni Sonpal joins Scott Hanselman to discuss the newest offering of the Azure Database family – Azure Database for PostgreSQL, which provides a managed database service for app development and deployment with a Postgres database in minutes and scale on the fly...

Ultimate Game Sale, Windows 10S, and more!

This Week on Windows we show off the Surface Laptop with Windows 10S, get in on the Fate of the Furious, and showcase the biggest game titles in the Ultimate Game Sale. Topics covered on this week's episode include:

  • SLING TV Cloud DVR ...
Build a chat app with SignalR Core - Mikael Mengistu

In this video Mikael Mengistu  shows us how to build a simple chat app with SignalR Core.

Create Spark Applications with the Azure Toolkit for IntelliJ

This quick 6 minute video will walk you through how to install and use the Azure Toolkit for IntelliJ to create Apache Spark applications in Scala and submitting it to an Azure HDInsight Spark cluster.

Power BI Embedded with the JavaScript SDK

Ran and Arina are back in the studio (actually, they never left :D ) to follow up on their first video to discuss the PowerBI JavaScript SDK. This SDK is a client-side API allows you to communicate with your report and interact with the report within your application as if it were a first-class citizen....

Short Video: How to open an Office 365 (Modern) Support Ticket

Are you facing an issue in Office 365 and need help to find a solution?

Watch this video to learn how our new Support Platform can help you find troubleshooting steps or how you can reach out to our support agents.

Episode 158: Functional Programming & F# with Lena Hall

We talk with Lena Hall about functional programming and F#. Which is faster, the mouse or keyboard? Walmart tells vendors to get off Amazon's cloud. And using AI to play Ms. Pac Man.

ASP.NET Monsters Ep 99: Front End Tools with David Wesst

There are countless front-end workflows. In what we're hoping will be the first of a series of episodes on workflows we talk with noted front-end guru and JavaScript master David Wesst (https://blog.davidwesst.com/). JavaScript or TypeScript? Gulp or Grunt? Hear what one expert thinks is the best combination.

KBYG: Microsoft Inspire

Going to Microsoft Inspire 2017 in Washington, DC?  Learn about what Voices for Innovation will be doing there.

Modern Dev Practices: Unit Testing

In this episode, Robert is joined by Phil Japikse, who explores how in modern development practices, unit testing is part of the development process, not a chore to be tackled after you write your code.

Phil spends most of the time on Test Driven Development (aka Test Driven Design), where you write a test first and then write just enough code to pass the test and then refine the code as you add more tests. In TDD, the tests embody the requirements the code must satisfy.  ...

Snack Pack 14: Upgrading Android Support Libraries

Welcome to The Xamarin Show Snack Pack Edition. A Snack Pack is bite sized episode that is focused on a specific topic and covered in just a few minutes. Today, we take a look at how to manage and upgrade the ever so important Android Support Libraries in Xamarin and Xamarin.Forms based applications....

Episode 3: England - Interview with Evelina Gabasova
In this episode of the MVP Show, we head to Cambridge, England, to visit post-doctoral researcher at MRC Cancer Unit, at the University of Cambridge, speaker and Microsoft MVP, Evelina Gabasova. We also had the chance to explore Cambridge, a university city, and the county town of Cambridgeshire, England, on the River Cam, about 50 miles north of London. The University of Cambridge was founded in 1209, a year after I was born. It is one of the top five universities in the world. I was lucky to stop in so she could drop the knowledge on data science, and educate me on an awesome data exploratory feature on Azure among other things. Watch the video to find out more!
Azure DocumentDB: a Deep Dive in to Advanced Features

Let's talk about how you can get the most out of Azure DocumentDB! We'll begin with an overview on what to think about when storing data in a document database covering:

  • What is data modeling and why should you care?...
Cancer Image Detection Competition Jumpstart

Since its launch in mid-January, the Data Science Bowl Lung Cancer Detection Competition has attracted more than 1,000 submissions. To be successful in this competition, data scientists need to be able to get started quickly and make rapid iterative changes.

In this webinar, we show how to compute features of the scanned images in the competition with a pre-trained Convolutional Neural Network (CNN) with Cognitive Toolkit (previously named CNTK), and use these features to classify the scans into cancerous or not cancerous, using a boosted tree with LightGBM library, all in one hour....

Join the Security Community

Learn how you can speak directly to Microsoft's security engineering teams to influence our products.

Introducing the Dream.Build.Play Game Developer Competition

Andrew Parsons joins the show to talk about a new competition being launched by Microsoft, called Dream.Build.Play. Dream.Build.Play is a game development competition that is open to all developers, working solo or in teams of up to seven. Create a Universal Windows Platform (UWP) game for one of the categories by December 31, 2017 and you'll get the chance to win cash prizes and show off your game to the world....

Introduction to MicrosoftML

MicrosoftML (MML) is a new machine learning package for Microsoft R Server. Microsoft R Server brings you the ability to do parallel and chunked data processing that addresses the restrictions of in-memory open source R. MML adds Microsoft's battle-tested algorithms and data transforms that are used by product teams across Microsoft. This brings new machine learning functionality with increased speed, performance and scale, especially for handling a large corpus of text data and high-dimensional categorical data....

Cognitive Services on Azure Government - Intelligent Mission

In this episode of the Azure Government video series, Steve Michelotti talks with Anna Roth (Senior Program Manager, Cognitive Services) about Cognitive Services in Azure Government. Anna discusses various aspects of how the technology works and how it enables developers to easily write code against the Cognitive Services APIs in any programming language they choose. In this presentation, you will see several demonstrations of Cognitive Services technologies that can greatly enhance the capabilities of government agencies.

Your Path to Partnering: Recommended Sessions at Microsoft Inspire 2017

The following is a guest post by Maritza Handal, Global ISV Co-Sell Lead within the One Commercial Partner (OCP) Organization at Microsoft.

Microsoft Inspire provides a wealth of information on various parts of our partners' businesses. But it can sometimes be a little daunting figuring out which sessions to attend in order to optimize your time there. With that in mind, we have created a guide to help partners navigate the sessions, specifically focused on partnering, according to their specific needs. To build your schedule, visit the Microsoft Inspire Session Catalog. To learn more about driving more sales through partnerships, read this post about how to build a robust channel program. Have a great time at Microsoft Inspire!

Changing Lives with Data Science and R at Microsoft

Whether it's called data science, machine learning, or predictive analytics, the combination of new data sources and statistical modeling has produced some truly revolutionary applications. Many of these applications incorporate open source technologies (including R) and research from academic institutions. In this talk, I'll share a few ways that Microsoft is improving the lives of people around the world by applying Statistics, research and open-source software in applications and devices, and describe how Microsoft has integrated R into its data platforms....

Spark Performance Tuning - Part 2

This week's Data Exposed show welcomes back Maxim Lukiyanov to talk more about Spark performance tuning with Spark 2.x. Maxim is a Senior PM on the big data HDInsight team and is in the studio today to present Part 2 of his 4-part series....

(Part 3) Hybrid Cloud for Enterprise Businesses

In this last video in a three part series on Hybrid Cloud, we will explore the value and efficiencies of hybrid cloud vs. fully on premise or fully cloud based scenarios for enterprise customers. Gain insights into how to increase developer productivity, SQL Server efficiency and peak network performance while reducing overall costs.  ...

Interview with Joy Chik, Corporate Vice President for the Identity Division at Microsoft

Soumow sits with Joy Chik, Corporate Vice President for the Identity Division in Microsoft's Cloud + Enterprise group. Joy's team is responsible for Active Directory, Azure Active Directory, Microsoft Account (MSA), Microsoft Graph, Identity Protection and Identity Management suites which are delivered to customers as cloud services or on-premises products....

SysSieve: Extracting Actionable Insights from Unstructured Text

Understanding free-form text is hard, be it bug reports or trouble tickets written by engineers or feedback/complaints from customers. We have built SysSieve, a learning system to do automated analysis of these important unstructured (yet incredibly noisy) data sources by building upon techniques from statistical NLP, ML, and information theory.  Today, this system is in production use across Windows, Bing, Skype, Office365 and CSS, as well as being leveraged to make platform improvements in our server and network hardware vendors. This video provides an overview of the SysSieve technical details and how it is being applied across product groups.

A Generic Framework for Mining Top-k Representative Subgraph Patterns

Mining subgraph patterns is an active area of research. Till now, the focus has primarily been on mining all subgraph patterns in the given database. However, due to the exponential subgraph search space, the number of patterns mined, typically, is too large for any human mediated analysis. Consequently, deriving insights from the mined patterns is hard for domain scientists. In addition, subgraph pattern mining is posed in multiple forms: the function that models if a subgraph is a pattern varies based on the application and the database could be over multiple graphs or a single, large graph....

Security Features on IoT Core

Security is a hot-button issue in the IoT space; IoT developers should be thinking about implementing hardware and software security features from the start of development. Windows 10 IoT Core provides several of these features to help protect your devices from network attacks as well as physical tampering. In this video, we discuss these features, how to turn them on, and why IoT device security is so important.

Defrag Tools #181 - System Power Report

In this episode of Defrag Tools, Chad Beeder and Andrew Richards are joined by Paresh Maisuria from the Windows Kernel Power team and Zach Holmes from the Fundamentals team to talk about System Power Report, a new feature in Windows 10 Creators Update....



          Frightenly accurate ‘mind reading’ A.I. is able to scan brains and guess what you’re thinking   
From medical applications like helping dermatologists diagnose skin cancer to teaching robots to get a better grip on the world around them, deep learning neural networks can carry out some pretty impressive tasks. Could mind reading be among them? The folks at Carnegie Mellon University certainly think so — and they’ve got the research to […]
          Comment on June Pieces Of My Mind #3 by Birger Johansson   
-A way to ease the workload when spotting stones that have been deliberately shaped as tools? And a boost for anthropologists searching for hominid bones among the millions of other fossils in the Olduwai ash. “New technique elucidates the inner workings of neural networks trained on visual data” https://phys.org/news/2017-06-technique-elucidates-neural-networks-visual.html Martin, if you dig up a cache of wehrmacht Pervetin you should be able to afford this kind of optronic tools.
          AWS GovCloud (US) and Amazon Rekognition – A Powerful Public Safety Tool   
I’ve already told you about and described how it uses deep neural network models to analyze images by detecting objects, scenes, and faces. Today I am happy to tell you that is now available in the AWS GovCloud (US) Region. To learn more, read the Amazon Rekognition FAQ, and the Amazon Rekognition Product Details, review […]
          Risto Miikulainen Wins Gabor Award   
Professor Risto Miikkulainen

Professor Risto Miikkulainen has won the 2017 Gabor Award from the International Neural Network Society (INNS), which recognizes the achievements of highly accomplished researchers in engineering applications of neural networks. Risto is on the forefront of neuroevolution—the evolution of neural networks using genetic algorithms. Risto and his team have shown that neuroevolution is a notab


          Stable Architectures for Deep Neural Networks. (arXiv:1705.03341v2 [cs.LG] UPDATED)   

Authors: Eldad Haber, Lars Ruthotto

Deep neural networks have become invaluable tools for supervised machine learning, e.g., classification of text or images. While often offering superior results over traditional techniques and successfully expressing complicated patterns in data, deep architectures are known to be challenging to design and train such that they generalize well to new data. Important issues with deep architectures are numerical instabilities in derivative-based learning algorithms commonly called exploding or vanishing gradients. In this paper we propose new forward propagation techniques inspired by systems of Ordinary Differential Equations (ODE) that overcome this challenge and lead to well-posed learning problems for arbitrarily deep networks.

The backbone of our approach is our interpretation of deep learning as a parameter estimation problem of nonlinear dynamical systems. Given this formulation, we analyze stability and well-posedness of deep learning and use this new understanding to develop new network architectures. We relate the exploding and vanishing gradient phenomenon to the stability of the discrete ODE and present several strategies for stabilizing deep learning for very deep networks. While our new architectures restrict the solution space, several numerical experiments show their competitiveness with state-of-the-art networks.


          (USA-WA-Seattle) Applied Scientist   
Applied Scientist Job ID 551723 Location US-WA-Seattle Posted Date 6/28/2017 Company Amazon Corporate LLC Position Category Machine Learning Science Recruiting Team .. Job DescriptionSeeking Applied Researchers to build the future of the Alexa Shopping Experience at Amazon. At Alexa Shopping, we strive to enable shopping in everyday life. We allow customers to instantly order whatever they need, by simply interacting with their smart devices such as Echo, Fire TV, and beyond. Our services allow you to shop, anywhere, easily without interrupting what you're doing - to go from "I want" to "It's on the way" in a matter of seconds. We are seeking the industry's best applied scientists to help us create new ways to shop. Join us, and help invent the future of everyday life. The products you would envision and craft require ambitious thinking and a tireless focus on inventing solution to solve customer problems. You must be passionate about creating algorithms and models that can scale to hundreds of millions of customers, and insanely curious about building new technology and unlocking its potential. The Alexa Shopping team is seeking an Applied Scientist who will partner with technology and business leaders to build new state-of-the-art algorithms, models and services that surprise and delight our voice customers. As part of the new Alexa Shopping team you will use ML techniques such as deep learning to create and put into production models that deliver personalized shopping recommendations, allow to answer customer questions and enable human-like dialogs with our devices. Basic QualificationsThe ideal candidate will have a PhD in Mathematics, Statistics, Machine Learning, Economics, or a related quantitative field, and 5+ years of relevant work experience, including: * Proven track record of achievements in natural language processing, search and personalization. * Expertize on a broad set of ML approaches and techniques, ranging from Artificial Neural Networks to Bayesian Non-Parametrics methods. * Experience in Structured Prediction and Dimensionality Reduction. * Strong fundamentals in problem solving, algorithm design and complexity analysis. * Proficiency in at least one scripting languages (e.g. Python) and one large-scale data processing platform (e.g. Hadoop, Hive, Spark). * Experience with using could technologies (e.g. S3, Dynamo DB, Elastic Search) and experience in data warehousing. * Strong personal interest in learning, researching, and creating new technologies with high commercial impact. Preferred Qualifications * Track record of peer reviewed academic publications. * Strong verbal/written communication skills, including an ability to effectively collaborate with both research and technical teams and earn the trust of senior stakeholders. Amazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation
          Neural Network Software Market 2017–2021 : Global Drivers, Opportunities, Trends, and Forecasts   
Neural network software is used in researching, stimulating, developing, and applying artificial neural networks (ANN) to several arrays of adaptive systems such as artificial intelligence (AI) and machine learning. Neural network simulators are software applications used in simulating the behavior of artificial or biological neural networks.