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advance-bayesian-modelling-with-pymc3

annotated_deep_learning_paper_implementations

🧑‍🏫 59 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

anomaly-reactionrl

Using RL for anomaly detection in NSL-KDD

awesome-kan

A comprehensive collection of KAN(Kolmogorov-Arnold Network)-related resources, including libraries, projects, tutorials, papers, and more, for researchers and developers in the Kolmogorov-Arnold Network field.

bayesdll

bayesian-machine-learning

Notebooks about Bayesian methods for machine learning

bayesian-neural-network-blogpost

Building a Bayesian deep learning classifier

bayesian-stats-modelling-tutorial

How to do Bayesian statistical modelling using numpy and PyMC3

bcnn_cancer_detection

Using Bayesian deep neural networks for classification of histopathological images.

deep-tda

deepmind-research

This repository contains implementations and illustrative code to accompany DeepMind publications

emergent-multiagent-strategies

Emergence of complex strategies through multiagent competition

functime

Time-series machine learning and embeddings at scale.

gs-quant

Python toolkit for quantitative finance

intro_stat_modeling_2017

Introduction to Statistical Modeling with Python (PyCon 2017)

kaggle-solutions

🏅 Collection of Kaggle Solutions and Ideas 🏅

llm-course

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

lstm-gan-

The LSTM GAN model can be used for generation of synthetic multi-dimension time series data.

machine-failure-detection

PCA and DBSCAN based anomaly and outlier detection method for time series data.

matsciml

Open MatSci ML Toolkit is a single framework for prototyping and scaling out deep learning models for materials discovery, built on top of OpenCatalyst, PyTorch Lightning, and the Deep Graph Library.

nksr

[CVPR 2023 Highlight] Neural Kernel Surface Reconstruction

notebooks

Ipython notebooks on various topics

pydata-2018

pydata-sf-2016-arima-tutorial

PyData San Francisco 2016 - ARIMA Tutorial

scipy2019-pmda-data

data and abstract for PMDA paper (SciPy 2019)

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.

the-incredible-pytorch

The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.

toponetx

Computing on Topological Domains

transformers-tutorials

This repository contains demos I made with the Transformers library by HuggingFace.

trl

Train transformer language models with reinforcement learning.