- An Overview of 3 Popular Courses on Deep Learning - Oct 13, 2017.
After completing the 3 most popular MOOCS in deep learning from Fast.ai, deeplearning.ai/Coursera (which is not completely released) and Udacity, I believe a post about what you can expect from these 3 courses will be useful for future Deep learning enthusiasts.
- How I started with learning AI in the last 2 months - Oct 9, 2017.
The relevance of a full stack developer will not be enough in the changing scenario of things. In the next two years, full stack will not be full stack without AI skills.
- Deep Learning for Object Detection: A Comprehensive Review - Oct 6, 2017.
By the end of this post, we will hopefully have gained an understanding of how deep learning is applied to object detection, and how these object detection models both inspire and diverge from one another.
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- A Guide For Time Series Prediction Using Recurrent Neural Networks (LSTMs) - Oct 5, 2017.
Looking at the strengths of a neural network, especially a recurrent neural network, I came up with the idea of predicting the exchange rate between the USD and the INR.
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- Neural Networks: Innumerable Architectures, One Fundamental Idea - Oct 4, 2017.
At the end of this post, you’ll be able to implement a neural network to identify handwritten digits using the MNIST dataset and have a rough time idea about how to build your own neural networks.
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- Understanding Machine Learning Algorithms - Oct 3, 2017.
Machine learning algorithms aren’t difficult to grasp if you understand the basic concepts. Here, a SAS data scientist describes the foundations for some of today’s popular algorithms.
- Key Takeaways from AI Conference in San Francisco 2017 – Day 2 - Oct 2, 2017.
Highlights and key takeaways from day 2 of AI Conference San Francisco 2017, including current state review, future trends, and top recommendations for AI initiatives.
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- Keras Cheat Sheet: Deep Learning in Python - Sep 27, 2017.
Keras is a Python deep learning library for Theano and TensorFlow. The package is easy to use and powerful, as it provides users with a high-level neural networks API to develop and evaluate deep learning models.
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- The Search for the Fastest Keras Deep Learning Backend - Sep 26, 2017.
This is an overview of the performance comparison for the popular Deep Learning frameworks supported by Keras – TensorFlow, CNTK, MXNet and Theano.
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- 30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets - Sep 22, 2017.
This collection of data science cheat sheets is not a cheat sheet dump, but a curated list of reference materials spanning a number of disciplines and tools.
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- Tensorflow Tutorial: Part 1 – Introduction - Sep 21, 2017.
Everyone is talking about Tensorflow these days. In this multipart series, we explain Tensorflow in detail, including it’s architecture and industry applications.
- 5 Ways to Get Started with Reinforcement Learning - Sep 20, 2017.
We give an accessible overview of reinforcement learning, including Deep Q Learning, and provide useful links for implementing RL.
- Keras Tutorial: Recognizing Tic-Tac-Toe Winners with Neural Networks - Sep 18, 2017.
In this tutorial, we will build a neural network with Keras to determine whether or not tic-tac-toe games have been won by player X for given endgame board configurations. Introductory neural network concerns are covered.
- Neural Network Foundations, Explained: Activation Function - Sep 13, 2017.
This is a very basic overview of activation functions in neural networks, intended to provide a very high level overview which can be read in a couple of minutes. This won't make you an expert, but it will give you a starting point toward actual understanding.
- Object Detection: An Overview in the Age of Deep Learning - Sep 13, 2017.
Like many other computer vision problems, there still isn’t an obvious or even “best” way to approach the problem of object recognition, meaning there’s still much room for improvement.
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- New Breakthroughs from DeepMind – Relational Networks and Visual Interaction Networks - Sep 12, 2017.
Such relational intelligence separates artificial intelligence systems with human cognition. DeepMind, the creators of AlphaGo, quietly published two groundbreaking research papers into this area, demonstrating a way to train relational reasoning using deep neural networks.
- Top /r/MachineLearning Posts, August: Andrew Ng is back at it; Reinforcement Learning makes a splash; Fixing your ANN - Sep 8, 2017.
Andrew Ng announces new Deep Learning specialization on Coursera; DeepMind and Blizzard open StarCraft II as an AI research environment; OpenAI bot beat best Dota 2 players in 1v1 at The International 2017; My Neural Network isn't working! What should I do?; Deep Learning Neural Networks Play Path of Exile
- Detecting Facial Features Using Deep Learning - Sep 4, 2017.
A challenging task in the past was detection of faces and their features like eyes, nose, mouth and even deriving emotions from their shapes. This task can be now “magically” solved by deep learning and any talented teenager can do it in a few hours.
- Top KDnuggets tweets, Aug 23-29: Python overtakes R, becomes the leader in #DataScience, #MachineLearning; I built a #chatbot in 2 hours - Aug 30, 2017.
Also: Recommendation System Algorithms Overview; The Connection Between #DataScience, #MachineLearning and #AI; The Ultimate Guide to Basic Data Cleaning.
- PyTorch or TensorFlow? - Aug 29, 2017.
PyTorch is better for rapid prototyping in research, for hobbyists and for small scale projects. TensorFlow is better for large-scale deployments, especially when cross-platform and embedded deployment is a consideration.
- An Intuitive Guide to Deep Network Architectures - Aug 28, 2017.
How and why do different Deep Learning models work? We provide an intuitive explanation for 3 very popular DL models: Resnet, Inception, and Xception.
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- 37 Reasons why your Neural Network is not working - Aug 22, 2017.
Over the course of many debugging sessions, I’ve compiled my experience along with the best ideas around in this handy list. I hope they would be useful to you.
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- Deep Learning and Neural Networks Primer: Basic Concepts for Beginners - Aug 18, 2017.
This is a collection of introductory posts which present a basic overview of neural networks and deep learning. Start by learning some key terminology and gaining an understanding through some curated resources. Then look at summarized important research in the field before looking at a pair of concise case studies.
- First Steps of Learning Deep Learning: Image Classification in Keras - Aug 16, 2017.
Whether you want to start learning deep learning for you career, to have a nice adventure (e.g. with detecting huggable objects) or to get insight into machines before they take over, this post is for you!
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- Explore The Future of Deep Learning with @teamrework - Aug 15, 2017.
Until recently, deep learning alluded to the big names in tech such as Amazon, Facebook, and Google as having a clear use for these tools. Whilst these are some of the key players in AI and DL implementation, there are also huge advantages for their applications in businesses and everyday enterprises.
- DeepMind Relational Reasoning Networks Demystified - Aug 15, 2017.
Every time DeepMind publishes a new paper, there is frenzied media coverage around it. We examine what is and is not real in recent work described as “DeepMind Neural Network Can Make Sense of Objects Around It”.
- Top /r/MachineLearning Posts, July: Friendly Suggestions re: Coding Practices; Racist AI How-To Without Really Trying - Aug 10, 2017.
Why can't you guys comment your f*cking code?; Train Chrome's Trex character to play independently; How to make a racist AI without really trying; Is training a NN to mimic a closed-source library legal?; 37 Reasons why your NN is not working
- How Convolutional Neural Networks Accomplish Image Recognition? - Aug 9, 2017.
Image recognition is very interesting and challenging field of study. Here we explain concepts, applications and techniques of image recognition using Convolutional Neural Networks.
- Mind Reading: Using Artificial Neural Nets to Predict Viewed Image Categories From EEG Readings - Aug 9, 2017.
This post outlines the approach taken at a recent deep learning hackathon, hosted by YCombinator-backed startup DeepGram. The dataset: EEG readings from a Stanford research project that predicted which category of images their test subjects were viewing using linear discriminant analysis.
- Train your Deep Learning Faster: FreezeOut - Aug 3, 2017.
We explain another novel method for much faster training of Deep Learning models by freezing the intermediate layers, and show that it has little or no effect on accuracy.
- Top KDnuggets tweets, Jul 26 – Aug 01: 37 Reasons why your #NeuralNetwork is not working; Machine Learning Exercises in Python - Aug 2, 2017.
Also Hill criteria for #causality vs #correlation via #xkcd cartoons; #MachineLearning Workflows in #Python from Scratch Part 2: k-means Clustering
- Visualizing Convolutional Neural Networks with Open-source Picasso - Aug 1, 2017.
Toolkits for standard neural network visualizations exist, along with tools for monitoring the training process, but are often tied to the deep learning framework. Could a general, easy-to-setup tool for generating standard visualizations provide a sanity check on the learning process?
- Introduction to Neural Networks, Advantages and Applications - Jul 25, 2017.
Artificial Neural Network (ANN) algorithm mimic the human brain to process information. Here we explain how human brain and ANN works.
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- Summary of Unintuitive Properties of Neural Networks - Jul 24, 2017.
Neural networks work really well on many problems, including language, image and speech recognition. However understanding how they work is not simple, and here is a summary of unusual and counter intuitive properties they have.
- Picking an Optimizer for Style Transfer - Jul 21, 2017.
Gradient Descent, Adam or Limited-memory Broyden–Fletcher–Goldfarb–Shanno? Which will optimize your style transfer neural network faster and better? Read this post for a data-backed discussion.
- Deep Learning, AI Assistant Summits London feature DeepMind and much more, Sep 21-22 – KDnuggets Offer - Jul 20, 2017.
The Deep Learning Summit London and the AI Assistant Summit London will be continuing the RE•WORK Global Summit Series this September 21 & 22. Early Bird discount is ending on July 28th. Register now to guarantee a spot at the Summit and use the discount code KDNUGGETS to save 20% on all tickets.
- Design by Evolution: How to evolve your neural network with AutoML - Jul 20, 2017.
The gist ( tl;dr): Time to evolve! I’m gonna give a basic example (in PyTorch) of using evolutionary algorithms to tune the hyper-parameters of a DNN.
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- 5 Free Resources for Getting Started with Deep Learning for Natural Language Processing - Jul 19, 2017.
This is a collection of 5 deep learning for natural language processing resources for the uninitiated, intended to open eyes to what is possible and to the current state of the art at the intersection of NLP and deep learning. It should also provide some idea of where to go next.
- Are Most Machine Learning Experts Turning to Deep Learning? - Jul 18, 2017.
Read a short opinion on what the impact of machine learning researchers focusing on deep learning will be.
- Top KDnuggets tweets, Jul 05-11: 10 Free Must-Read Books for #MachineLearning and #DataScience; Why AI and Machine Learning? - Jul 12, 2017.
Also great overview: Unintuitive properties of #NeuralNetworks; #Apache #Flink vs #Spark: The Strange Loop in #DeepLearning - the coolest idea in #MachineLearning in 20 yrs;
- Medical Image Analysis with Deep Learning , Part 4 - Jul 11, 2017.
This is the fourth installment of this series, and covers medical images and their components, medical image formats and their format conversions. The goal is to develop knowledge to help us with our ultimate goal — medical image analysis with deep learning.
- The Strange Loop in Deep Learning - Jul 11, 2017.
This ‘strange loop’ is in fact is the fundamental reason for what Yann LeCun describes as “the coolest idea in machine learning in the last twenty years.”
- Applying Deep Learning to Real-world Problems - Jun 30, 2017.
In this blog post I shared three learnings that are important to us at Merantix when applying deep learning to real-world problems. I hope that these ideas are helpful for other people who plan to use deep learning in their business.
- Using the TensorFlow API: An Introductory Tutorial Series - Jun 28, 2017.
This post summarizes and links to a great multi-part tutorial series on learning the TensorFlow API for building a variety of neural networks, as well as a bonus tutorial on backpropagation from the beginning.
- Deep Learning with R + Keras - Jun 27, 2017.
Keras has grown in popularity and supported on a wide set of platforms including Tensorflow, CNTK, Apple’s CoreML, and Theano. It is becoming the de factor language for deep learning.
- Taxonomy of Methods for Deep Meta Learning - Jun 22, 2017.
This post discusses a variety of contemporary Deep Meta Learning methods, in which meta-data is manipulated to generate simulated architectures. Current meta-learning capabilities involve either support for search for architectures or networks inside networks.
- Understanding Deep Learning Requires Re-thinking Generalization - Jun 16, 2017.
What is it that distinguishes neural networks that generalize well from those that don’t? A satisfying answer to this question would not only help to make neural networks more interpretable, but it might also lead to more principled and reliable model architecture design.
- Medical Image Analysis with Deep Learning , Part 3 - Jun 15, 2017.
In this article we will focus — basic deep learning using Keras and Theano. We will do 2 examples one using keras for basic predictive analytics and other a simple example of image analysis using VGG.
- Deep Learning Papers Reading Roadmap - Jun 13, 2017.
The roadmap is constructed in accordance with the following four guidelines: from outline to detail; from old to state-of-the-art; from generic to specific areas; focus on state-of-the-art.
- Top /r/MachineLearning Posts, May: Deep Image Analogy; Stylized Facial Animations; Google Open Sources Sketch-RNN - Jun 9, 2017.
Deep Image Analogy; Example-Based Synthesis of Stylized Facial Animations; Google releases dataset of 50M vector drawings, open sources Sketch-RNN implementation; New massive medical image dataset coming from Stanford; Everything that Works Works Because it's Bayesian: Why Deep Nets Generalize?
- Why Does Deep Learning Not Have a Local Minimum? - Jun 2, 2017.
"As I understand, the chance of having a derivative zero in each of the thousands of direction is low. Is there some other reason besides this?"
- An Introduction to the MXNet Python API - May 26, 2017.
This post outlines an entire 6-part tutorial series on the MXNet deep learning library and its Python API. In-depth and descriptive, this is a great guide for anyone looking to start leveraging this powerful neural network library.
- The Two Phases of Gradient Descent in Deep Learning - May 12, 2017.
In short, you reach different resting placing with different SGD algorithms. That is, different SGDs just give you differing convergence rates due to different strategies, but we do expect that they all end up at the same results!
- Top 10 Recent AI videos on YouTube - May 10, 2017.
Top viewed videos on artificial intelligence since 2016 include great talks and lecture series from MIT and Caltech, Google Tech Talks on AI.
- Using Deep Learning To Extract Knowledge From Job Descriptions - May 9, 2017.
We present a deep learning approach to extract knowledge from a large amount of data from the recruitment space. A learning to rank approach is followed to train a convolutional neural network to generate job title and job description embeddings.
- Building, Training, and Improving on Existing Recurrent Neural Networks - May 8, 2017.
In this post, we’ll provide a short tutorial for training a RNN for speech recognition, including code snippets throughout.
- Top 10 Machine Learning Videos on YouTube, updated - May 3, 2017.
The top machine learning videos on YouTube include lecture series from Stanford and Caltech, Google Tech Talks on deep learning, using machine learning to play Mario and Hearthstone, and detecting NHL goals from live streams.
- KDnuggets™ News 17:n17, May 3: Learn Machine Learning… in 10 Days?!? Gradient Descent, Simplified - May 3, 2017.
How to Learn Machine Learning in 10 Days; Keep it simple! How to understand Gradient Descent algorithm; The Guerrilla Guide to Machine Learning with Python; What Data You Analyzed - KDnuggets Poll Results and Trends; Cartoon: Machine Learning - What They Think I Do
- Deep Learning – Past, Present, and Future - May 2, 2017.
There is a lot of buzz around deep learning technology. First developed in the 1940s, deep learning was meant to simulate neural networks found in brains, but in the last decade 3 key developments have unleashed its potential.
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- One Deep Learning Virtual Machine to Rule Them All - Apr 28, 2017.
The frontend code of programming languages only needs to parse and translate source code to an intermediate representation (IR). Deep Learning frameworks will eventually need their own “IR.”
- How to Build a Recurrent Neural Network in TensorFlow - Apr 26, 2017.
This is a no-nonsense overview of implementing a recurrent neural network (RNN) in TensorFlow. Both theory and practice are covered concisely, and the end result is running TensorFlow RNN code.
- Awesome Deep Learning: Most Cited Deep Learning Papers - Apr 21, 2017.
This post introduces a curated list of the most cited deep learning papers (since 2012), provides the inclusion criteria, shares a few entry examples, and points to the full listing for those interested in investigating further.
- Negative Results on Negative Images: Major Flaw in Deep Learning? - Apr 20, 2017.
This is an overview of recent research outlining the limitations of the capabilities of image recognition using deep neural networks. But should this really be considered a "limitation?"
- Medical Image Analysis with Deep Learning , Part 2 - Apr 13, 2017.
In this article we will talk about basics of deep learning from the lens of Convolutional Neural Nets. We plan to use this knowledge to build CNNs in the next post and use Keras to develop a model to predict lung cancer.
- 5 Machine Learning Projects You Can No Longer Overlook, April - Apr 13, 2017.
It's about that time again... 5 more machine learning or machine learning-related projects you may not yet have heard of, but may want to consider checking out. Find tools for data exploration, topic modeling, high-level APIs, and feature selection herein.
- Medical Image Analysis with Deep Learning - Apr 6, 2017.
In this article, I start with basics of image processing, basics of medical image format data and visualize some medical data.
- Deep Learning, Generative Adversarial Networks & Boxing – Toward a Fundamental Understanding - Mar 28, 2017.
In this post we will see why GANs have so much potential, and frame GANs as a boxing match between two opponents.
- Cooperative Trust Among Neural Networks Drives Deeper Learning - Feb 28, 2017.
Machine learning developers need to model a growing range of multi-partner scenarios where many learning agents and data sources interact under varying degrees of trustworthiness. This IBM site helps to take next step towards continuous intelligence.
- An Overview of Python Deep Learning Frameworks - Feb 27, 2017.
Read this concise overview of leading Python deep learning frameworks, including Theano, Lasagne, Blocks, TensorFlow, Keras, MXNet, and PyTorch.
- The Anatomy of Deep Learning Frameworks - Feb 24, 2017.
This post sketches out some common principles which would help you better understand deep learning frameworks, and provides a guide on how to implement your own deep learning framework as well.
- The Gentlest Introduction to Tensorflow – Part 4 - Feb 22, 2017.
This post is the fourth entry in a series dedicated to introducing newcomers to TensorFlow in the gentlest possible manner, and focuses on logistic regression for classifying the digits of 0-9.
- The Gentlest Introduction to Tensorflow – Part 3 - Feb 21, 2017.
This post is the third entry in a series dedicated to introducing newcomers to TensorFlow in the gentlest possible manner. This entry progresses to multi-feature linear regression.
- Turbo Charge Agile Processes with Deep Learning - Feb 7, 2017.
The key to leveraging Deep Learning, or more broadly AI, in the workplace is to understand where it fits within an agile development environment.
- Top arXiv Papers, January: ConvNets Advances, Wide Instead of Deep, Adversarial Networks Win, Learning to Reinforcement Learn - Feb 3, 2017.
Check out the top arXiv Papers from January, covering convolutional neural network advances, why wide may trump deep, generative adversarial networks, learning to reinforcement learn, and more.
- Deep Learning Research Review: Natural Language Processing - Jan 31, 2017.
This edition of Deep Learning Research Review explains recent research papers in Natural Language Processing (NLP). If you don't have the time to read the top papers yourself, or need an overview of NLP with Deep Learning, this post is for you.
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- Creating Curious Machines: Building Information-seeking Agents - Jan 24, 2017.
Researchers at Maluuba are developing ways to teach artificial agents how to seek information actively, by asking questions. This includes a deep neural agent that learns to accomplish these tasks through efficient information-seeking behaviour, a vital step towards Artificial General Intelligence.
- Deep Learning Can be Applied to Natural Language Processing - Jan 16, 2017.
This post is a rebuttal to a recent article suggesting that neural networks cannot be applied to natural language given that language is not a produced as a result of continuous function. The post delves into some additional points on deep learning as well.
- 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.
- 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.
- 5 Machine Learning Projects You Can No Longer Overlook, January - Jan 2, 2017.
There are a lot of popular machine learning projects out there, but many more that are not. Which of these are actively developed and worth checking out? Here is an offering of 5 such projects, the most recent in an ongoing series.
- 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.
- KDnuggets™ News 16:n44, Dec 14: Key Data Science 2016 Events, 2017 Trends; Where Data Science was applied; Bayesian Basics - Dec 14, 2016.
Data Science, Predictive Analytics Main Developments in 2016, Key Trends in 2017; Where Analytics, Data Mining, Data Science were applied in 2016; Bayesian Basics, Explained; Data Science Trends To Look Out For In 2017; Artificial Neural Networks (ANN) Introduction
- 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.
- Artificial Neural Networks (ANN) Introduction, Part 1 - Dec 8, 2016.
This intro to ANNs will look at how we can train an algorithm to recognize images of handwritten digits. We will be using the images from the famous MNIST (Mixed National Institute of Standards and Technology) database.
- Top KDnuggets tweets, Nov 30 – Dec 06: A great and useful collection of minimal and clean implementations of #MachineLearning algorithms - Dec 7, 2016.
Also: #MachineLearning Yearning book draft, Free Download, by Andrew Ng; A short guide to learn #NeuralNets, and maybe get famous and rich with #DeepLearning; Free Book: Foundations of Computer Science, Aho & Ullman.
- 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 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.
- 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.
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- A Quick Introduction to Neural Networks - Nov 9, 2016.
This article provides a beginner level introduction to multilayer perceptron and backpropagation.
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- 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.
- Top /r/MachineLearning Posts, October: NSFW Image Recognition, Differentiable Neural Computers, Hinton on Coursera - Nov 4, 2016.
NSFW Image Recognition, Differentiable Neural Computers, Hinton's Neural Networks for Machine Learning Coursera course; Introducing the AI Open Network; Making a Self-driving RC Car
- KDnuggets™ News 16:n38, Oct 26: Free Machine Learning EBooks; Neural Networks in Python with Scikit-learn - Oct 26, 2016.
5 EBooks to Read Before Getting into A Machine Learning Career; A Beginner's Guide to Neural Networks with Python and Scikit-learn 0.18!; New Poll: What was the largest dataset you analyzed / data mined?; Jupyter Notebook Best Practices for Data Science
- A Beginner’s Guide to Neural Networks with Python and SciKit Learn 0.18! - Oct 20, 2016.
This post outlines setting up a neural network in Python using Scikit-learn, the latest version of which now has built in support for Neural Network models.
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- 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
- 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.
- 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.
- Neural Designer: Predictive Analytics Software - Sep 26, 2016.
Neural Designer advanced neural network algorithms, combined with a simple user interface and fast performance, make it a great tool for data scientists. Download free 15-day trial version.
- 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.
- Top KDnuggets tweets, Sep 14-20: Why we need #DataScience: brain wont let us see 12 black dots at intersection - Sep 21, 2016.
Why we need #DataScience: brain won't let us see 12 black dots at intersections; #Blurring sensitive info no longer safe! #MachineLearning can recover originals ;Pokemon Go Data; The #NeuralNetwork Zoo - Great chart of different configurations.
- 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.
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- 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.
- 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.
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- 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.
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- 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.
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- Top KDnuggets tweets, Aug 24-30: #DataScientist – sexiest job of the 21st century until …; Activation Function in #NeuralNetworks. - Aug 31, 2016.
Cartoon: #DataScientist - sexiest job of the 21st century until ...; What is the Role of the Activation Function in Neural Networks?; LinkedIn Machine Learning team tutorial on building #Recommender system; Create a #Chatbot for #Telegram in #Python to Summarize Text.
- How Convolutional Neural Networks Work - Aug 31, 2016.
Get an overview of what is going on inside convolutional neural networks, and what it is that makes them so effective.
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- What is the Role of the Activation Function in a Neural Network? - Aug 30, 2016.
Confused as to exactly what the activation function in a neural network does? Read this overview, and check out the handy cheat sheet at the end.
- 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
- Making Data Science Accessible – Neural Networks - Aug 11, 2016.
This post attempts to make the underlying concepts of neural networks more accessible to everyone. Gain a high level view of their working here.
- A Beginner’s Guide to Neural Networks with R! - Aug 11, 2016.
In this article we will learn how Neural Networks work and how to implement them with the R programming language! We will see how we can easily create Neural Networks with R and even visualize them. Basic understanding of R is necessary to understand this article.
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- 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.
- Top KDnuggets tweets, Jul 27 – Aug 2: Understanding neural networks with Google TensorFlow Playground; Getting Started with Data Science in Python - Aug 3, 2016.
Understanding neural networks with Google TensorFlow Playground; The 100 Best-Funded #Analytics #DataScience #Startups; Great tutorial: Getting Started with #DataScience - #Python; #MachineLearning over 1M hotel reviews: interesting insights.
- 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.
- Multi-Task Learning in Tensorflow: Part 1 - Jul 20, 2016.
A discussion and step-by-step tutorial on how to use Tensorflow graphs for multi-task learning.
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- 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.
- 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.
- 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.
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- Recursive (not Recurrent!) Neural Networks in TensorFlow - Jun 30, 2016.
Learn how to implement recursive neural networks in TensorFlow, which can be used to learn tree-like structures, or directed acyclic graphs.
- 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.
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- KDnuggets™ News 16:n22, Jun 22: Data Science Blog Contest; Free Machine Learning Ebook; Master SQL for Data Science - Jun 22, 2016.
Data Science Blog Contest; New Free Andrew Ng Machine Learning Book Under Construction; 7 Steps to Mastering SQL for Data Science; A Visual Explanation of the Back Propagation Algorithm; Mining Twitter Data with Python Part 1: Collecting Data