What’s New in the 3rd Edition
A brief summary of what’s new in the 3rd edition of Python Machine Learning.
deep-learning, Machine Learning
Research
Deep double descent
We show that the double descent phenomenon occurs in CNNs, ResNets, and transformers: performance first improves, then gets worse, and then improves again with increasing model size, data size, or training time. This effect is often avoided through car…
Research
Procgen Benchmark
We’re releasing Procgen Benchmark, 16 simple-to-use procedurally-generated environments which provide a direct measure of how quickly a reinforcement learning agent learns generalizable skills.
deep-learning, graphics
Differentiable Path Tracing on the GPU/TPU
You can download a PDF (typset in LaTeX) of this blog post here.
Jupyter Notebook Code on GitHub: https://github.com/ericjang/pt-jax
This blog post is a tutorial on implementing path tracing, a physically-based rendering algorithm, in JAX. …
Safety & Alignment
Safety Gym
We’re releasing Safety Gym, a suite of environments and tools for measuring progress towards reinforcement learning agents that respect safety constraints while training.
Uncategorised
Self-Supervised Representation Learning
[Updated on 2020-01-09: add a new section on Contrastive Predictive Coding].
[Updated on 2020-04-13: add a “Momentum Contrast” section on MoCo, SimCLR and CURL.]
[Updated on 2020-07-08: add a “Bisimulation” section on DeepMDP…
finance, leverage
Robinhood, Leverage, and Lemonade
DISCLAIMER: NO INVESTMENT OR LEGAL ADVICEThe Content is for informational purposes only, you should not construe any such information or other material as legal, tax, investment, financial, or other advice. Investing involves risk, please consult a fin…
Research
GPT-2: 1.5B release
As the final model release of GPT-2’s staged release, we’re releasing the largest version (1.5B parameters) of GPT-2 along with code and model weights to facilitate detection of outputs of GPT-2 models. While there have been larger language models rele…
Uncategorised
Computing Receptive Fields of Convolutional Neural Networks
Detailed derivations and open-source code to analyze the receptive fields of convnets.