• Machine Creativity Beats Some Modern Art
  If machines can outperform humans at playing games and driving cars, can they also produce better art? A new kind of Turing test aims to find out...

• Performance RNN: Generating Music with Expressive Timing and Dynamics
  We present Performance RNN, an LSTM-based recurrent neural network designed to model polyphonic music with expressive timing and dynamics. Here’s an example generated by the model...

• Data animation shows the unprecedented development of China’s rail system
  Less than 20 years ago, only three cities in China had subways. If you didn’t live in Beijing, Guangzhou, or Shanghai, underground travel was a complete mystery. Today, there are more than 60 metro lines (paywall) in 25 cities, making subterranean transit accessible to some 291 million people....

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For over 43 years, doctoral level scientists, social scientists, engineers, and health/medical professionals have applied their knowledge and technical expertise to policymaking at the national and international levels. Fellows serve yearlong assignments in all three branches of the federal government and represent a broad range of backgrounds, disciplines and career stages.
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• Meow Generator
  I experimented with generating faces of cats using Generative adversarial networks (GAN). I wanted to try DCGAN, WGAN and WGAN-GP in low and higher resolutions. I used the CAT dataset (yes this is a real thing!) for my training sample...

• Using Deep Learning to Reconstruct High-Resolution Audio
  Inspired by the successful applications of deep learning to image super-resolution, there is recent interest in using deep neural networks to accomplish this upsampling on raw audio waveforms. After prototyping several methods, I focused on implementing and customizing recently published research from the 2017 International Conference on Learning Representations...

• ai.bythebay.io: Chris Moody, AI at Stitch Fix
  I'll review applied deep learning techniques we use at Stitch Fix to understand our client's personal style. Interpretable deep learning models are not only useful to scientists, but lead to better client experiences -- no one wants to interact with a black box virtual assistant. We do this in several ways...

• Google Stakes Its Future on a Piece of Software
  Alphabet thinks it can wrest the cloud computing market away from Amazon by helping companies make use of machine learning with a tool called TensorFlow...

• Be smarter. Be seetd
  We have built upon our previous seating arrangement efforts and developed a new seating allocation tool - “seetd”. It works by casting the allocation of people to seats (or offices) as an optimization problem, with several different cost-terms which we will discuss below. The ultimate goal is to then find an arrangement which minimises our overall cost function...

• General Electric Builds an AI Workforce
  As part of its shift toward high-tech businesses, the 125-year-old company is threading artificial intelligence throughout its operations, starting with its scientists....

• Python Solver for the On-Time Arrival Problem in Traffic Congestion
  SOTA-Py is a Python-based solver for the policy- and path-based "SOTA" problems, using the algorithm(s) described in Tractable Pathfinding for the Stochastic On-Time Arrival Problem and previous works referenced therein. What is the SOTA problem? Read on...

• Noisy Networks for Exploration
  We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to its weights, and show that the induced stochasticity of the agent's policy can be used to aid efficient exploration...

• Effectively Using Matplotlib
  Now that I have taken the time to learn some of these tools and how to use them with matplotlib, I have started to see matplotlib as an indispensable tool. This post will show how I use matplotlib and provide some recommendations for users getting started or users who have not taken the time to learn matplotlib...

• Probabilistic programming from scratch
  An introduction to probabilistic programming that neither assumes nor uses statistics, and should be accessible to anyone who can code a little. Bayesian inference without the math or any libraries if you like...

• How to Handle Imbalanced Classes in Machine Learning
  In this guide, we’ll explore 5 effective ways to handle imbalanced classes...

• Hypothesis Testing: A Visual Introduction To Statistical Significance
  "If you are looking for a short beginners guide packed with visual examples, this booklet is for you"......
  For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page...