[unable to retrieve full-text content]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.
Original Post: Deep Learning and Neural Networks Primer: Basic Concepts for Beginners
[unable to retrieve full-text content]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!
Original Post: First Steps of Learning Deep Learning: Image Classification in Keras
[unable to retrieve full-text content]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.
Original Post: Explore The Future of Deep Learning with @teamrework
[unable to retrieve full-text content]What is Relational Reasoning in deep learning? Here we explain it in details.
Original Post: DeepMind Relational Reasoning Networks Demystified
[unable to retrieve full-text content]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
Original Post: Top /r/MachineLearning Posts, July: Friendly Suggestions re: Coding Practices; Racist AI How-To Without Really Trying
[unable to retrieve full-text content]Image recognition is very interesting and challenging field of study. Here we explain concepts, applications and techniques of image recognition using Convolutional Neural Networks.
Original Post: How Convolutional Neural Networks Accomplish Image Recognition?
[unable to retrieve full-text content]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.
Original Post: Mind Reading: Using Artificial Neural Nets to Predict Viewed Image Categories From EEG Readings
[unable to retrieve full-text content]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.
Original Post: Train your Deep Learning Faster: FreezeOut
[unable to retrieve full-text content]Also Hill criteria for #causality vs #correlation via #xkcd cartoons; #MachineLearning Workflows in #Python from Scratch Part 2: k-means Clustering
Original Post: Top KDnuggets tweets, Jul 26 – Aug 01: 37 Reasons why your #NeuralNetwork is not working; Machine Learning Exercises in Python
[unable to retrieve full-text content]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?
Original Post: Visualizing Convolutional Neural Networks with Open-source Picasso