dylanthomas's Stars
openai/CLIP
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
google-deepmind/deepmind-research
This repository contains implementations and illustrative code to accompany DeepMind publications
probml/pyprobml
Python code for "Probabilistic Machine learning" book by Kevin Murphy
borisbanushev/stockpredictionai
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
locuslab/TCN
Sequence modeling benchmarks and temporal convolutional networks
sktime/pytorch-forecasting
Time series forecasting with PyTorch
facebookresearch/jepa
PyTorch code and models for V-JEPA self-supervised learning from video.
AI4Finance-Foundation/FinRL-Trading
For trading. Please star.
peerchemist/finta
Common financial technical indicators implemented in Pandas.
JavierAntoran/Bayesian-Neural-Networks
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
vahidk/EffectivePyTorch
PyTorch tutorials and best practices.
idiap/fast-transformers
Pytorch library for fast transformer implementations
sintel-dev/Orion
Library for detecting anomalies in signals
leegao/readme2tex
Renders TeXy Math for Github Readmes
reinforcement-learning-kr/lets-do-irl
Inverse RL algorithms (APP, MaxEnt, GAIL, VAIL)
facebookresearch/rebel
An algorithm that generalizes the paradigm of self-play reinforcement learning and search to imperfect-information games.
numba/numba-examples
Example Numba implementations of functions
mattriemer/MER
Fork of the GEM project (https://github.com/facebookresearch/GradientEpisodicMemory) including Meta-Experience Replay (MER) methods from the ICLR 2019 paper (https://openreview.net/pdf?id=B1gTShAct7)
tencent-ailab/tleague_projpage
hihihihiwsf/AST
Adversarial Sparse Transformer for Time Series Forecasting
williamgilpin/fnn
Embed strange attractors using a regularizer for autoencoders
ayaabdelsalam91/TS-Interpretability-Benchmark
vincent-leguen/STRIPE
Code for our NeurIPS 2020 paper "Probabilistic Time Series Forecasting with Structured Shape and Temporal Diversity"
facebookresearch/level-replay
This code implements Prioritized Level Replay, a method for sampling training levels for reinforcement learning agents that exploits the fact that not all levels are equally useful for agents to learn from during training.
henripal/sgld
swyoon/normalized-autoencoders
The official repository for <Autoencoding Under Normalization Constraints> (Yoon, Noh and Park, ICML 2021).
reinforcement-learning-kr/reinforcement-learning-pytorch
Minimal and Clean Reinforcement Learning Examples in PyTorch
insujeon/Hello-Generative-Model
PyTorch를 활용한 Generative Model 입문 CAMP (실습자료)
kcome/SwiftIB
openias/openias.github.io
Blog for the Open Institute for Advanced Study