• Google's Learning Software Learns to Write Software
  Artificial-intelligence researchers at Google are trying to automate the tasks of highly paid workers more likely to wear a hoodie than a coat and tie—themselves. In a project called AutoML, Google’s researchers have taught machine-learning software to build machine-learning software...

• Your Brain Limits You to Just Five BFFs
  The number of people we can have meaningful contact with is limited by the size of our brains. Now this group seems to be subdivided into layers, say anthropologists...

• Generalization in Deep Learning
  This paper explains why deep learning can generalize well, despite large capacity and possible algorithmic instability, nonrobustness, and sharp minima, effectively addressing an open problem in the literature...

 
Attend the Future Labs AI Summit in NYC on October 30 – 31

Two days of technical trainings and talks from leading executives at Google, NASA, Vector Institute, NYU, AI4ALL, and more. Day 1 courses will be taught by experts from Intel, Amazon Web Services (AWS), Insight Data Science, Paperspace, and NYU, and include courses such as intro to machine learning and deep learning, machine learning for statisticians, and building and deploying deep learning models on AWS. Day 2 presentations will cover investing in AI, democratizing AI, the current state of quantum computing, social and ethical impacts of the technology, and more. Sign up before prices rise on October 25!
   

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• You Could Become an AI Master Before You Know It. Here’s How.
  Automating machine learning will make the technology more accessible to non–AI experts...

• Ranking Popular Deep Learning Libraries for Data Science
  Below is a ranking of 23 open-source deep learning libraries that are useful for Data Science, based on Github and Stack Overflow activity, as well as Google search results...

• Advice For New and Junior Data Scientists
  Two years ago, I shared my experience on doing data science in the industry. The writing was originally meant to be a private reflection for myself to celebrate my two year twitterversary at Twitter, but I instead published it on Medium because I believe it could be very useful for many aspiring data scientists. Fast forward to 2017, I have been working at Airbnb for a little bit less than two years and have recently become a senior data scientist — an industry title used to signal that one has acquired a certain level of technical expertise. As I reflect on my journey so far and imagine what’s next to come, I once again wrote down a few lessons that I wish I had known in the early days of my career....

• Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow
  This is a recorded version of the VAST Best Paper Talk at VIS2017...

• Coloring B&W portraits with neural networks.
  Earlier this year, Amir Avni used neural networks to troll the subreddit /r/Colorization - a community where people colorize historical black and white images manually using Photoshop. They were astonished with Amir’s deep learning bot - what could take up to a month of manual labour could now be done in just a few seconds. I was fascinated by Amir’s neural network, so I reproduced it and documented the process. First off, let’s look at some of the results/failures from my experiments...

• High-Resolution Multi-Scale Neural Texture Synthesis
  If you’re doing neural texture synthesis, use a multi-scale Gaussian pyramid representation and everything will look better!...

• Neural Networks for Advertisers
  Recently I came across a problem to solve using some sort of machine learning capabilities, which was the need to count the total time during which a specific company was advertised on the various places at a football match...

• Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
  We propose a unified formulation for the problem of 3D human pose estimation from a single raw RGB image that reasons jointly about 2D joint estimation and 3D pose reconstruction to improve both tasks. We take an integrated approach that fuses probabilistic knowledge of 3D human pose with a multi-stage CNN architecture and uses the knowledge of plausible 3D landmark locations to refine the search for better 2D locations. The entire process is trained end-to-end, is extremely efficient and obtains state-of-the-art results on Human3.6M outperforming previous approaches both on 2D and 3D errors...

• skimr: A frictionless, pipeable approach to dealing with summary statistics
  Provides summary statistics that you can skim quickly to understand and your data and see what may be missing. It handles different data types (numerics, factors, etc), and returns a skimr object that can be piped or displayed nicely for the human reader....

• Luminoth: An open source toolkit for computer vision
  Easily train neural networks to detect and classify objects with custom data. Use state of the art models such as Faster R-CNN...

• Streaming Dataframes
  This post describes a prototype project to handle continuous data sources of tabular data using Pandas and Streamz...

• Statistics Done Wrong: The Woefully Complete Guide
  "... a pithy, essential guide to statistical blunders in modern science that will show you how to keep your research blunder-free..."...
  For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page...