dukeprashanth's repositories
annotated_deep_learning_paper_implementations
๐งโ๐ซ 59 Implementations/tutorials of deep learning papers with side-by-side notes ๐; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), ๐ฎ reinforcement learning (ppo, dqn), capsnet, distillation, ... ๐ง
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.
bayesian-machine-learning
Notebooks about Bayesian methods for machine learning
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.
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
๐ Collection of Kaggle Solutions and Ideas ๐
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
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.
tutorials
CatBoost tutorials repository
VRP
Backtesting the thesis paper entitled: Trading volatility Trading strategies based on the VIX term structure