# Deep Learning (1051)

**Deep Learning Summit in San Francisco, Jan 26-27 (KDnuggets Offer)**- Jan 5, 2017.

Discover advances in Deep Learning, NLP, speech recognition, image retrieval, virtual assistants, and more from leading researchers and industry at the Deep Learning Summit and Virtual Assistant Summit in San Francisco, 26-27 January. Use code KDNUGGETS to save 20%.**The Major Advancements in Deep Learning in 2016**- Jan 5, 2017.

Get a concise overview of the major advancements observed in deep learning over the past year.**Top /r/MachineLearning Posts, December: OpenAI Universe; Deep Learning MOOC For Coders; Musk: Tesla Gets Awesome-er**- Jan 5, 2017.

OpenAI Universe; Deep Learning For Coders—18 hours of lessons for free; Elon Musk on Twitter: Tesla Autopilot vision neural net now working well; Apple to Start Publishing AI Research; Duolingo's "half-life regression" method for modeling human memory**Top KDnuggets tweets, Dec 21 – Jan 03: R vs Python: A Comparison and Free Books to Learn; Popular Deep Learning Tools – a review**- Jan 4, 2017.

R vs Python: A Comparison and Free Books to Learn; The Five Capability Levels of Deep Learning - Yann Lecun view; The Future Of Machine Learning, McKinsey 2016 Analytics Study; #BigData: Main Developments in 2016 and Key Trends in 2017**Generative Adversarial Networks – Hot Topic in Machine Learning**- Jan 3, 2017.

What is Generative Adversarial Networks (GAN) ? A very illustrative explanation of GAN is presented here with simple examples like predicting next frame in video sequence or predicting next word while typing in google search.**Game Theory Reveals the Future of Deep Learning**- Dec 29, 2016.

This post covers the emergence of Game Theoretic concepts in the design of newer deep learning architectures. Deep learning systems need to be adaptive to imperfect knowledge and coordinating systems, 2 areas with which game theory can help.**KDnuggets™ News 16:n46, Dec 28: 4 Reasons Your Machine Learning Model is Wrong; Deep Learning for coders MOOC**- Dec 28, 2016.

First Deep Learning for coders MOOC launched by Jeremy Howard; 4 Reasons Your Machine Learning Model is Wrong; 5 Capability Levels of Deep Learning Intelligence; Data Science Basics: Power Laws and Distributions.**The Five Capability Levels of Deep Learning Intelligence**- Dec 22, 2016.

Deep learning writer Carlos Perez gives his own classification for deep learning-based AI, which is aimed at practitioners. This classification gives us a sense of where we currently are and where we might be heading.**First Deep Learning for coders MOOC launched by Jeremy Howard**- Dec 21, 2016.

Leading Data Scientist and entrepreneur Jeremy Howard launches a free Deep Learning course that shows end-to-end how to get state of the art results, including a top place in a Kaggle competition.**Mark van Rijmenam’s Top 7 Big Data Trends for 2017**- Dec 20, 2016.

Top Big Data expert Mark van Rijmenam weighs in with his top Big Data-related predictions for 2017.**Predictions for Deep Learning in 2017**- Dec 19, 2016.

The first hugely successful consumer application of deep learning will come to market, a dominant open-source deep-learning tool and library will take the developer community by storm, and more Deep Learning predictions.**ResNets, HighwayNets, and DenseNets, Oh My!**- Dec 19, 2016.

This post walks through the logic behind three recent deep learning architectures: ResNet, HighwayNet, and DenseNet. Each make it more possible to successfully trainable deep networks by overcoming the limitations of traditional network design.**Deep Learning Works Great Because the Universe, Physics and the Game of Go are Vastly Simpler than Prior Models and Have Exploitable Patterns**- Dec 16, 2016.

How is Deep Learning experiencing such success solving complex problems? Deep Learning is useful and powerful but it is also that the problems were not as big or as hard as researchers feared when they were unsolved.**Top KDnuggets tweets, Dec 7-13: Want to learn Numpy? A Github repo of Numpy learning exercises**- Dec 14, 2016.

Also Deep Learning Roadmap: "Which paper should I start reading from?"; Free ebooks: #MachineLearning with #Python and Practical Data Analysis; Daily plan for studying to become a Google software engineer.**Achieving Human Parity in Conversational Speech Recognition**- Dec 13, 2016.

This is an overview of the paper which outlines, for the first time, a system has been developed that exceeds human performance in one of the most difficult of all human speech recognition tasks: natural conversations held over the telephone.**arXiv Paper Spotlight: Why Does Deep and Cheap Learning Work So Well?**- Dec 13, 2016.

The recent paper at hand approaches explaining deep learning from a different perspective, that of physics, and discusses the role of "cheap learning" (parameter reduction) and how it relates back to this innovative perspective.**New Book: TensorFlow for Machine Intelligence – KDnuggets Holiday Offer**- Dec 12, 2016.

TensorFlow for Machine Intelligence is a hands-on introduction to learning algorithms and the "TensorFlow book for humans." For a limited holiday special, KDnuggets readers get a 40% discount, available here.**Artificial Neural Networks (ANN) Introduction, Part 2**- Dec 9, 2016.

Matching the performance of a human brain is a difficult feat, but techniques have been developed to improve the performance of neural network algorithms, 3 of which are discussed in this post: Distortion, mini-batch gradient descent, and dropout.**KDnuggets™ News 16:n43, Dec 7: Where did you use Data Science? The hard thing about Deep Learning; Big Data Main Events in 2016, Key Trends for 2017**- Dec 7, 2016.

Where did you apply Analytics, Data Science in 2016? Big Data Main Developments in 2016 and Key Trends in 2017; The Data Science Delusion; The hard thing about deep learning.**Why Deep Learning is Radically Different From Machine Learning**- Dec 5, 2016.

There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL), yet the distinction is very clear to practitioners in these fields. Are you able to articulate the difference?**The hard thing about deep learning**- Dec 1, 2016.

It’s easy to optimize simple neural networks, let’s say single layer perceptron. But, as network becomes deeper, the optmization problem becomes crucial. This article discusses about such optimization problems with deep neural networks.**Deep Learning Research Review: Reinforcement Learning**- Nov 25, 2016.

This edition of Deep Learning Research Review explains recent research papers in Reinforcement Learning (RL). If you don't have the time to read the top papers yourself, or need an overview of RL in general, this post has you covered.**Top KDnuggets tweets, Nov 16-22: Top 20 #Python #MachineLearning #OpenSource Projects; Shortcomings of #DeepLearning**- Nov 23, 2016.

Top 20 #Python #MachineLearning #OpenSource Projects; Shortcomings of #DeepLearning; What is the Difference Between #DeepLearning and Regular #MachineLearning?; Questions To Ask When Moving #MachineLearning From Practice to Production; How to Choose the Right #Database System**Implementing a CNN for Human Activity Recognition in Tensorflow**- Nov 21, 2016.

In this post, we will see how to employ Convolutional Neural Network (CNN) for HAR, that will learn complex features automatically from the raw accelerometer signal to differentiate between different activities of daily life.**Questions To Ask When Moving Machine Learning From Practice to Production**- Nov 18, 2016.

An overview of applying machine learning techniques to solve problems in production. This articles covers some of the varied questions to ponder when incorporating machine learning into teams and processes.**Deep Learning Reading Group: Skip-Thought Vectors**- Nov 17, 2016.

Skip-thought vectors take inspiration from Word2Vec skip-gram and attempt to extend it to sentences, and are created using an encoder-decoder model. Read on for an overview of the paper.**The Foundations of Algorithmic Bias**- Nov 16, 2016.

We might hope that algorithmic decision making would be free of biases. But increasingly, the public is starting to realize that machine learning systems can exhibit these same biases and more. In this post, we look at precisely how that happens.**Shortcomings of Deep Learning**- Nov 15, 2016.

Current Deep Learning successes such as AlphaGo rely on massive amount of labeled data, which is easy to get in games, but often hard in other contexts. You can't play 20 questions with nature and win!**An Intuitive Explanation of Convolutional Neural Networks**- Nov 11, 2016.

This article provides a easy to understand introduction to what convolutional neural networks are and how they work.**A Quick Introduction to Neural Networks**- Nov 9, 2016.

This article provides a beginner level introduction to multilayer perceptron and backpropagation.**Deep Learning cleans podcast episodes from ‘ahem’ sounds**- Nov 8, 2016.

“3.5 mm audio jack… Ahem!!” where did you hear that? ;) Well, this post is not about Google Pixel vs iPhone 7, but how to remove ugly “Ahem” sound from a speech using deep convolutional neural network. I must say, very interesting read.**Deep Learning Research Review: Generative Adversarial Nets**- Oct 31, 2016.

This edition of Deep Learning Research Review explains recent research papers in the deep learning subfield of Generative Adversarial Networks. Don't have time to read some of the top papers? Get the overview here.**KDnuggets Top Blogger: An Interview with Adit Deshpande, Deep Learning Aficionado**- Oct 31, 2016.

Read an interview with KDnuggets Top Blogger Adit Deshpande, a deep learning aficionado and masterful blogger, who also just happens to be a second year undergraduate student.**5 EBooks to Read Before Getting into A Machine Learning Career**- Oct 21, 2016.

A carefully-curated list of 5 free ebooks to help you better understand the various aspects of what machine learning, and skills necessary for a career in the field.**NVIDIA: Deep Learning Library Software Development Engineer**- Oct 20, 2016.

To researchers and companies are using GPUs to power a revolution in deep learning, enabling breakthroughs in problems from image classification to speech recognition to natural language processing. Join the team which is building software which will be used by the entire world.**Top KDnuggets tweets, Oct 12-18: #DeepLearning Key Terms, Explained; Free Foundations of #DataScience text PDF**- Oct 20, 2016.

#DeepLearning Key Terms, Explained; Free Foundations of #DataScience text PDF; Top 12 Interesting Careers to Explore in #BigData; #ICYMI The 10 Algorithms #MachineLearning Engineers Need to Know**NVIDIA: Developer Technology Engineer – Autonomous Driving**- Oct 19, 2016.

Seeking a Developer Technology Engineer – Autonomous Driving to be a member of the automotive team. The candidate will be responsible for working with cutting-edge applications of Deep Learning, computer vision and image processing on NVIDIA’s next-generation automotive products.**NVIDIA: Solution Architect (Eastern Region)**- Oct 19, 2016.

Seeking a world-class engineer/scientist for an exciting role as a Solutions Architect. Work with the most exciting high-performance computing hardware, software and impactful projects.**NVIDIA: Senior Deep Learning R&D Engineer – Autonomous Driving**- Oct 19, 2016.

Seeking a Senior Deep Learning R&D Engineer, with the opportunity to innovate from algorithms, to system design, to processor architecture and see your work used in cars all over the world.**NVIDIA: Senior Research Scientist (Deep Learning)**- Oct 19, 2016.

Seeking a Senior Research Scientist (Deep Learning) to conceive deep learning approaches to solving particular product problems, construct and curate large problem specific datasets, and design and implement machine learning techniques aimed at solving specific problems.**KDnuggets™ News 16:n37, Oct 19: Top Data Science Videos; 12 Interesting Big Data Careers; Deep Learning Key Terms**- Oct 19, 2016.

Top 10 Data Science Videos on YouTube; Top 12 Interesting Careers to Explore in Big Data; Deep Learning Key Terms, Explained; Artificial Intelligence, Deep Learning, and Neural Networks, Explained; MLDB: The Machine Learning Database**Deep Learning meets Deep Deployment**- Oct 17, 2016.

We now have a deep learning model that is able to deliver valuable results, but how can we apply it easily to new data where and when we need to?**Artificial Intelligence, Deep Learning, and Neural Networks, Explained**- Oct 14, 2016.

This article is meant to explain the concepts of AI, deep learning, and neural networks at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.**Top /r/MachineLearning Posts, September: Open Images Dataset; Whopping Deep Learning Grant; Advanced ML Courseware**- Oct 7, 2016.

Google Research announces the Open Images dataset; Canadian Government Deep Learning Research grant; DeepMind: WaveNet - A Generative Model for Raw Audio; Machine Learning in a Year - From total noob to using it at work; Phd-level machine learning courses; xkcd: Linear Regression**Statistics.com new courses: Anomaly Detection, Meta Analysis, IoT, Deep Learning, Spatial Analytics**- Oct 5, 2016.

Five new courses from Statistics.com, fully online and asynchronous - interact with leading experts in private forums. Use promo code “kdn2016” for $50 off any course.**Predicting Future Human Behavior with Deep Learning**- Sep 30, 2016.

Carl Vondrick, MIT researcher, who studies computer vision and machine learning, discusses how to use Big Data with minimal annotations and applications to predictive vision and scene understanding.**Deep Learning Reading Group: SqueezeNet**- Sep 29, 2016.

This paper introduces a small CNN architecture called “SqueezeNet” that achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters.**Deep Learning Singapore & Machine Intelligence NYC – KDnuggets Offer**- Sep 28, 2016.

Explore the latest machine learning research, technology and applications, with 2 RE.WORK events: the Deep Learning Summit in Singapore (20-21 Oct) and the Machine Intelligence Summit in New York City (Nov 2-3). Use code KDNUGGETS for 20% off.**Data Science for Internet of Things (IoT): Ten Differences From Traditional Data Science**- Sep 26, 2016.

The connected devices (The Internet of Things) generate more than 2.5 quintillion bytes of data daily. All this data will significantly impact business processes and the Data Science for IoT will take increasingly central role. Here we outline 10 main differences between Data Science for IoT and traditional Data Science.**Up to Speed on Deep Learning: August Update, Part 2**- Sep 23, 2016.

This is the second part of an overview of deep learning stories that made news in August. Look to see if you have missed anything.**Deep Learning Reading Group: Deep Residual Learning for Image Recognition**- Sep 22, 2016.

Published in 2015, today's paper offers a new architecture for Convolution Networks, one which has since become a staple in neural network implementation. Read all about it here.**Up to Speed on Deep Learning: August Update**- Sep 21, 2016.

Check out this thorough roundup of deep learning stories that made news in August, and see if there are any items of note that you missed.**9 Key Deep Learning Papers, Explained**- Sep 20, 2016.

If you are interested in understanding the current state of deep learning, this post outlines and thoroughly summarizes 9 of the most influential contemporary papers in the field.**Deep Learning Reading Group: Deep Compression**- Sep 15, 2016.

An concise overview of a paper covering three methods of compressing a neural network in order to reduce the size of the network on disk, improve performance, and decrease run time.**Top KDnuggets tweets, Sep 07-13: Dask for #Parallel Programming; Computationally generated Average Face**- Sep 14, 2016.

Computationally generated Average Face; Dask for #Parallel Programming; The (Not So) New #DataScientist Venn Diagram; Human in #AI loop - #DeepLearning lets you take an image of a dress and show...**The Deception of Supervised Learning**- Sep 13, 2016.

Do models or offline datasets ever really tell us what to do? Most application of supervised learning is predicated on this deception.**Urban Sound Classification with Neural Networks in Tensorflow**- Sep 12, 2016.

This post discuss techniques of feature extraction from sound in Python using open source library Librosa and implements a Neural Network in Tensorflow to categories urban sounds, including car horns, children playing, dogs bark, and more.**Deep Learning Reading Group: Deep Networks with Stochastic Depth**- Sep 8, 2016.

An concise overview of a recent paper which introduces a new way to perturb networks during training in order to improve their performance, stochastic depth networks.**A Beginner’s Guide To Understanding Convolutional Neural Networks Part 2**- Sep 8, 2016.

This is the second part of a thorough introductory treatment of convolutional neural networks. Have a look after reading the first part.**Up to Speed on Deep Learning: July Update, Part 2**- Sep 7, 2016.

Check out this second installation of deep learning stories that made news in July. See if there are any items of note you missed.**KDnuggets™ News 16:n32, Sep 7: Cartoon: Data Scientist was sexiest job until…; Up to Speed on Deep Learning**- Sep 7, 2016.

Cartoon: Data Scientist - the sexiest job of the 21st century until...; Up to Speed on Deep Learning: July Update; How Convolutional Neural Networks Work; Learning from Imbalanced Classes; What is the Role of the Activation Function in a Neural Network?**A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1**- Sep 6, 2016.

Interested in better understanding convolutional neural networks? Check out this first part of a very comprehensive overview of the topic.**Top /r/MachineLearning Posts, August: Google Brain AMA, Image Completion with TensorFlow, Japanese Cucumber Farming**- Sep 5, 2016.

Google Brain AMA; Image Completion with Deep Learning in TensorFlow; Japanese Cucumber Farming; Andrew Ng's machine learning class in Python; Google Brain datasets for robotics research**New Book: TensorFlow for Machine Intelligence, KDnuggets Offer**- Aug 30, 2016.

TensorFlow for Machine Intelligence is a hands-on introduction to learning algorithms and the "TensorFlow book for humans." KDnuggets readers get a 25% discount, available here.**Up to Speed on Deep Learning: July Update**- Aug 29, 2016.

Check out this thorough roundup of deep learning stories that made news in July. See if there are any items of note you missed.**Is “Artificial Intelligence” Dead? Long Live Deep Learning?!?**- Aug 26, 2016.

Has Deep Learning become synonymous with Artificial Intelligence? Read a discussion on the topic fuelled by the opinions of 7 participating experts, and gain some additional insight into the future of research and technology.**Analytics, Security, Deep Learning, IoT, Data Science Online Courses**- Aug 20, 2016.

Upcoming online courses include : Statistical and machine learning methods for detecting anomalies, identifying images, and processing data from sensors; Deep Learning; Internet of Things (IoT): Programming for Analytics; and Meta Analysis in R.**The Gentlest Introduction to Tensorflow – Part 2**- Aug 19, 2016.

Check out the second and final part of this introductory tutorial to TensorFlow.**Top Machine Learning Projects for Julia**- Aug 19, 2016.

Julia is gaining traction as a legitimate alternative programming language for analytics tasks. Learn more about these 5 machine learning related projects.**The Gentlest Introduction to Tensorflow – Part 1**- Aug 17, 2016.

In this series of articles, we present the gentlest introduction to Tensorflow that starts off by showing how to do linear regression for a single feature problem, and expand from there.**KDnuggets™ News 16:n30, Aug 17: Why Deep Learning Works; Neural Networks with R; Central Limit Theorem for Data Science**- Aug 17, 2016.

3 Thoughts on Why Deep Learning Works So Well; A Beginner's Guide to Neural Networks with R!; Central Limit Theorem for Data Science; Cartoon: Make Data Great Again**Tales from ICML: Insights and Takeaways**- Aug 15, 2016.

The dust has settled from ICML 2016, having been held in June in NYC. Read some perspective on what was offered at the conference and relevant takeaways from a reflective attendee.**Stop Blaming Terminator for Bad AI Journalism**- Aug 11, 2016.

Too often, we blame The Terminator for the public's misconceptions concerning machine learning. But do James Cameron and the Austrian Oak stand wrongfully accused?**Top KDnuggets tweets, Aug 03-09: Understanding the Bias-Variance Tradeoff: An Overview**- Aug 10, 2016.

Understanding the Bias-Variance Tradeoff: An Overview; Cartoon: Facebook #DataScience experiments and Cats; Bayesian #Machine Learning, Explained; Deep Reinforcement Learning for Keras.**3 Thoughts on Why Deep Learning Works So Well**- Aug 10, 2016.

While answering a posed question in his recent Quora Session, Yann LeCun also shared 3 high-level thoughts on why deep learning works so well.**7 Steps to Understanding Computer Vision**- Aug 9, 2016.

A starting point for Computer Vision and how to get going deeper. Dive into this post for some overview of the right resources and a little bit of advice.**Trulia (Zillow Group): Data Scientist – Computer Vision & Deep Learning**- Aug 7, 2016.

Become one of the founding members of computer vision/deep learning group at Trulia and develop solutions that would be used by millions of users across Zillow Group.**KDnuggets™ News 16:n28, Aug 3: Data Science Stats 101; Understanding NoSQL Databases; Core of Data Science**- Aug 3, 2016.

Data Science Statistics 101; 7 Steps to Understanding NoSQL Databases; The Core of Data Science; Data Science for Beginners 2: Is your data ready?**Yann LeCun Quora Session Overview**- Aug 1, 2016.

Here is a quick oversight, with excerpts, of the Yann LeCun Quora Session which took place on Thursday July 28, 2016.**Deep Learning For Chatbots, Part 2 – Implementing A Retrieval-Based Model In TensorFlow**- Jul 29, 2016.

Check out part 2 of this tutorial on building chatbots with deep neural networks. This part gets practical, and using Python and TensorFlow to implement.**Top KDnuggets tweets, Jul 20-26: Math-free simple explanation: #DeepLearning Demystified; Are #Humans Becoming More Machine-Like?**- Jul 27, 2016.

Finally, a #TensorFlow book for humans; Great math-free simple intro explanation video: Deep Learning Demystified; Does #sentiment analysis work? A tidy analysis of Yelp reviews; JupyterLab: the next generation of the #Jupyter Notebook**Why Do Deep Learning Networks Scale?**- Jul 25, 2016.

A discussion of what about deep learning architectures allows them to scale, and addresses some assumptions that often inhibit an understanding of this topic.**Introducing Cloud Hosted Deep Learning Models**- Jul 21, 2016.

Algorithmia introduces a solution for hosting and distributing locally-trained deep learning models on Algorithmia using GPUs in the cloud, where they become smart API endpoints for other developers to use.**KDnuggets™ News 16:n26, Jul 20: Bayesian Machine Learning, Explained; Start Learning Deep Learning; Big Data is in Trouble**- Jul 20, 2016.

Bayesian Machine Learning, Explained; How to Start Learning Deep Learning; Why Big Data is in Trouble: They Forgot About Applied Statistics; Data Mining/Data Science "Nobel Prize": 2016 SIGKDD Innovation Award to Philip S. Yu**In Deep Learning, Architecture Engineering is the New Feature Engineering**- Jul 19, 2016.

A discussion of architecture engineering in deep neural networks, and its relationship with feature engineering.**How to Start Learning Deep Learning**- Jul 14, 2016.

Want to get started learning deep learning? Sure you do! Check out this great overview, advice, and list of resources.**KDnuggets™ News 16:n25, Jul 13: Top Machine Learning MOOCs; 5 Deep Learning Projects; Support Vector Machines Overview**- Jul 13, 2016.

Top Machine Learning MOOCs and Online Lectures: A Comprehensive Overview; Support Vector Machines: A Simple Explanation; 5 Deep Learning Projects You Can No Longer Overlook; Why You Should Attend the Data Science Summit 2016 and 9 Talks To Be Excited About**Semi-supervised Feature Transfer: The Practical Benefit of Deep Learning Today?**- Jul 12, 2016.

This post evaluates four different strategies for solving a problem with machine learning, where customized models built from semi-supervised "deep" features using transfer learning outperform models built from scratch, and rival state-of-the-art methods.**5 Deep Learning Projects You Can No Longer Overlook**- Jul 12, 2016.

There are a number of "mainstream" deep learning projects out there, but many more niche projects flying under the radar. Have a look at 5 such projects worth checking out.**Top Machine Learning MOOCs and Online Lectures: A Comprehensive Survey**- Jul 11, 2016.

This post reviews Machine Learning MOOCs and online lectures for both the novice and expert audience.**Deep Residual Networks for Image Classification with Python + NumPy**- Jul 7, 2016.

This post outlines the results of an innovative Deep Residual Network implementation for Image Classification using Python and NumPy.**Three Impactful Machine Learning Topics at ICML 2016**- Jul 1, 2016.

This post discusses 3 particular tutorial sessions of impact from the recent ICML 2016 conference held in New York. Check out some innovative ideas on Deep Residual Networks, Memory Networks for Language Understanding, and Non-Convex Optimization.**Peeking Inside Convolutional Neural Networks**- Jun 29, 2016.

This post discusses using some tricks to peek inside of the neural network, and to visualize what the individual units in a layer detect.**Top Machine Learning Libraries for Javascript**- Jun 24, 2016.

Javascript may not be the conventional choice for machine learning, but there is no reason it cannot be used for such tasks. Here are the top libraries to facilitate machine learning in Javascript.**A Review of Popular Deep Learning Models**- Jun 21, 2016.

This post is a concise overview of a few of the more interesting popular deep learning models to have appeared over the past year. Get up to speed and try a few of the models out for yourself.**Top KDnuggets tweets, Jun 8-14: All-in-one Docker image for Deep Learning; Good Book list for Data lovers**- Jun 15, 2016.

Good Book list for #Data lovers; OpenAI - a living collection of important and fun problems; All-in-one #Docker image for #DeepLearning; 10 Useful #Python #DataVisualization Libraries for Any Discipline;**Figuring Out the Algorithms of Intelligence**- Jun 15, 2016.

Marvin Minsky, the father of AI, passed away this year. One of his inventions was the confocal microscope, which we used to take this high-resolution picture of a live brain circuit. Something in these cells allows them to automatically identify useful connections and establish useful networks out of information.**What Big Data, Data Science, Deep Learning software goes together?**- Jun 14, 2016.

We analyze the associations between top Data Science tools, Commercial vs Free/Open Source, rank tools on R vs Python bias, find tools more associated with Big Data, those more associated with Deep Learning, and uncover strong regional differences.**Top KDnuggets tweets, Jun 1-7: “Deep” vs “Regular” Machine Learning; Introduction to Scientific Python – NumPy**- Jun 8, 2016.

How to Build Your Own #DeepLearning Box; What is the Difference Between #DeepLearning and "Regular" #MachineLearning? Data Science of #Variable Selection: A Review; Why choose #Python for #MachineLearning?**Deep Learning, Pachinko, and James Watt: Efficiency is the Driver of Uncertainty**- Jun 8, 2016.

A reasoned discussion of why the next generation of data efficient learning approaches rely on us developing new algorithms that can propagate stochasticity or uncertainty right through the model, and which are mathematically more involved than the standard approaches.**KDnuggets™ News 16:n20, Jun 8: R, Python Duel for 1st Place; “Regular” Machine Learning vs Deep Learning; Numpy Intro**- Jun 8, 2016.

R, Python Duel As Top Analytics, Data Science software; What is the Difference Between Deep Learning and "Regular" Machine Learning; An Introduction to Scientific Python; How to Build Your Own Deep Learning Box**The Truth About Deep Learning**- Jun 6, 2016.

An honest look at deep learning, what it is**not**, its advantages over "shallow" neural networks, and some of the common assumptions and conflations that surround it.**What is the Difference Between Deep Learning and “Regular” Machine Learning?**- Jun 3, 2016.

Another concise explanation of a machine learning concept by Sebastian Raschka. This time, Sebastian explains the difference between Deep Learning and "regular" machine learning.**How to Build Your Own Deep Learning Box**- Jun 2, 2016.

Want to build an affordable deep learning box and get all the required software installed? Read on for a proper overview.**Introduction to Recurrent Networks in TensorFlow**- May 31, 2016.

A straightforward, introductory overview of implementing Recurrent Neural Networks in TensorFlow.**Let Me Hear Your Voice and I’ll Tell You How You Feel**- May 24, 2016.

This post provides an overview of a voice tone analyzer implemented as part of a cohesive emotion detection system, directly from the researcher and architect.**The Good, Bad & Ugly of TensorFlow**- May 24, 2016.

A survey of six months of rapid evolution (+ tips/hacks and code to fix the ugly stuff) using TensorFlow. Get some great advice from the trenches.**5 Machine Learning Projects You Can No Longer Overlook**- May 19, 2016.

We all know the big machine learning projects out there: Scikit-learn, TensorFlow, Theano, etc. But what about the smaller niche projects that are actively developed, providing useful services to users? Here are 5 such projects.**KDnuggets™ News 16:n18, May 18: Annual Software Poll; Practical Data Science Skills; Creative Deep Learning**- May 18, 2016.

Poll: What software you used for Analytics, Data Mining, Data Science; Practical skills that practical data scientists need; Are Deep Neural Networks Creative?; TPOT : A Python Tool for Automating Data Science**Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?**- May 14, 2016.

Vote in KDnuggets 17th Annual Poll: What software you used for Analytics, Data Mining, Data Science Machine Learning projects in the past 12 months? We will clean and analyze the results and publish our analysis afterwards.**Troubleshooting Neural Networks: What is Wrong When My Error Increases?**- May 13, 2016.

An overview of some of the things that could lead to an increased error rate in neural network implementations.**Are Deep Neural Networks Creative?**- May 12, 2016.

Deep neural networks routinely generate images and synthesize text. But does this amount to creativity? Can we reasonably claim that deep learning produces art?**Deep Learning and Neuromorphic Chips**- May 12, 2016.

The 3 main ingredients to creating artificial intelligence are hardware, software, and data, and while we have focused historically on improving software and data, what if, instead, the hardware was drastically changed?**Top Talks and Tutorials From PyData London**- May 11, 2016.

Get some insight into the most recent Python data science talks and presentations with this eclectic mix of videos from PyData London 2016.**How to Quantize Neural Networks with TensorFlow**- May 4, 2016.

The simplest motivation for quantization is to shrink neural network representation by storing the min and max for each layer. Learn more how to perform quantization for deep neural networks.**Top /r/MachineLearning Posts, April: New Google Machine Learning Videos, Deep Learning Book, TensorFlow Playground**- May 2, 2016.

Check out the most popular topics on Reddit's Machine Learning subreddit from April, including TensorFlow, deep learning, tutorials, self-reflection, and free books.**Machine Learning for Artists – Video lectures and notes**- Apr 28, 2016.

Art has always been deep for those who appreciate it... but now, more than ever, deep learning is making a real impact on the art world. Check out this graduate course, and its freely-available resources, focusing on this very topic.**Top KDnuggets tweets, Apr 12-26: The Race For AI: Google, Facebook, Amazon, Apple; Comprehensive Guide to Learning #Python**- Apr 27, 2016.

Data Science helps see where your country will stand in WW 3; Recommender Systems: New Comprehensive Textbook; Good read: Deep Learning in Neural Networks - extreme summary; The Race For #AI: Google, Facebook, Amazon, Apple rush to grab #AI startups.**KDnuggets™ News 16:n15, Apr 27: Deep Learning vs. SVMs, Random Forests; Python Guide for Data Science**- Apr 27, 2016.

When Does Deep Learning Work Better Than SVMs or Random Forests; Comprehensive Guide to Learning Python for Data Science; Top 10 IPython Notebook Tutorials for Data Science and Machine Learning; 5,000 KDnuggets Posts - Examining Our Most Popular Content**When Does Deep Learning Work Better Than SVMs or Random Forests®?**- Apr 22, 2016.

Some advice on when a deep neural network may or may not outperform Support Vector Machines or Random Forests.**Top 10 IPython Notebook Tutorials for Data Science and Machine Learning**- Apr 22, 2016.

A list of 10 useful Github repositories made up of IPython (Jupyter) notebooks, focused on teaching data science and machine learning. Python is the clear target here, but general principles are transferable.**Deep Learning for Chatbots, Part 1 – Introduction**- Apr 19, 2016.

The first in a series of tutorial posts on using Deep Learning for chatbots, this covers some of the techniques being used to build conversational agents, and goes from the current state of affairs through to what is and is not possible.**Top 15 Frameworks for Machine Learning Experts**- Apr 19, 2016.

Either you are a researcher, start-up or big organization who wants to use machine learning, you will need the right tools to make it happen. Here is a list of the most popular frameworks for machine learning.**Weekend Reading List: Free eBooks and Other Online Resources**- Apr 16, 2016.

Do you have free time for reading this weekend? Here are a few new (or refreshed) selections of varying length for your leisure, along with a pair of papers, one cutting edge, and one classic.**Top KDnuggets tweets, Apr 6-11: Using Machine Learning to predict Villanova win; Deep Learning for Beginners – notes by research leaders**- Apr 13, 2016.

Deep Learning for Beginners - notes by research leaders ; Here is how Matt Harvey used #MachineLearning to predict #Villanova win; Brilliant #Dilbert cartoon: Management class on NOT listening finally pays off; #DeepLearning: 5 People To Know @AndrewYNg @Ylecun @karpathy @Peteskomoroch; #use Text Control: No formatting, No Character Formatting, Template: Default**KDnuggets™ News 16:n13, Apr 13: Signs of a Bad Data Scientist; Deep Learning from 30,000 Feet; Analytics Survey**- Apr 13, 2016.

10 Signs Of A Bad Data Scientist; Deep Learning from 30,000 feet; IDC/KDnuggets Advanced Analytics Survey - please participate; A Pocket Guide to Data Science; Basics of GPU Computing for Data Scientists**New Deep Learning Book Finished, Finalized Online Version Available**- Apr 12, 2016.

What will likely become known as the seminal book on deep learning is finally finished, with the online version finalized and freely-accessible to all those interested in mastering deep neural networks.**Deep Learning from 30,000 feet**- Apr 9, 2016.

My very-high level overview of Deep Learning for Delta Sky Magazine, including neurons, a conspiracy, games, amazing feats of superhuman ability, and more - appropriate for reading at 30,000 feet.**Tricking Deep Learning**- Apr 8, 2016.

Deep neural networks have had remarkable success with many tasks including image recognition. Read this overview regarding deep learning trickery, and why you should be cognizant.**Stochastic Depth Networks Accelerate Deep Network Training**- Apr 7, 2016.

Read about the presentation and overview of a new deep neural network architectural method, and the response to some strong reaction that it brought about.