roshni-kamath

roshni-kamath's Stars

• labmlai/annotated_deep_learning_paper_implementations

  🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

  Language:Python58.6k4651356k

• mit-han-lab/once-for-all

  [ICLR 2020] Once for All: Train One Network and Specialize it for Efficient Deployment

  Language:Python1.9k5375338

• daviwil/emacs-from-scratch

  An example of a fully custom Emacs configuration developed live on YouTube!

  Language:Emacs Lisp1.8k4672308

• huyvnphan/PyTorch_CIFAR10

  Pretrained TorchVision models on CIFAR10 dataset (with weights)

  Language:Python658718155

• SkafteNicki/dtu_mlops

  Exercises and supplementary material for the machine learning operations course at DTU.

  Language:Python65176541

• brynhayder/reinforcement_learning_an_introduction

  Notes and exercise solutions for second edition of Sutton & Barto's book

  Language:TeX37281178

• diegoalejogm/Reinforcement-Learning

  Implementation of Reinforcement Learning algorithms in Python, based on Sutton's & Barto's Book (Ed. 2)

  Language:Jupyter Notebook1566441

• antoyang/NAS-Benchmark

  [ICLR 2020] NAS evaluation is frustratingly hard

  Language:Python1494724

• YinTat/optimizationbook

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• kamperh/bayes_gmm

  Bayesian Gaussian mixture models in Python.

  Language:Python644122

• FlorianMarquardt/advanced_machine_learning

  Code to go along the lecture course "Advanced Machine Learning for Physics, Science, and Artificial Scientific Discovery"

  Language:Jupyter Notebook526020

• waynemystir/stanford-cs229

  These are my solutions to the problem sets for Stanford's Machine Learning class - cs229

  Language:Jupyter Notebook483120

• cdebacco/MultiTensor

  Multilayer network tensor factorization, for community detection, link prediction and measure layer interdependence.

  Language:C++416113

• blei-lab/ars-reparameterization

  Source code for Naesseth et. al. "Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms" (2017)

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• APMLA-2021/APMLA-WS_21-22_material

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• felixriese/hyperspectral-regression

  Code examples for the book chapter "Supervised, Semi-Supervised and Unsupervised Learning for Hyperspectral Regression".

  Language:Jupyter Notebook27205

• damienlancry/DBAL

  Implementation of Deep Bayesian Active Learning with Image Data with modAL (python module for Active Learning)

  Language:Jupyter Notebook26347

• gebob19/introduction_to_normalizing_flows

  Jupyter Notebook corresponding to 'Going with the Flow: An Introduction to Normalizing Flows'

  Language:Jupyter Notebook250110

• timrudner/S-FSVI

  Code for the paper 'Continual Learning via Sequential Function-Space Variational Inference'

  Language:Jupyter Notebook22227

• fonsp/disorganised-mess

  fun pluto notebooks

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• muzimuzhi/personal-notes

  11503

• rashed091/Probabilistic-Graphical-Models

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• sreahw/cam-notes

  My Cambridge Lecture Notes

  Language:TeX8104

• Daisuke-Kanaizumi/q-special-functions

  programs for q-special functions and q-series

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• iceychris/edge-popup

  :lollipop: Unofficial reproduction of the paper "What's Hidden in a Randomly Weighted Neural Network?"

  Language:Jupyter Notebook6304

• terrencewayne/Paper-notes

  This is a repository to record my notes about reading papers.

  41

• chsasank/design-patterns

  Design patterns in python

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• Sekeh-Lab/InformationFlow-CL

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• dieterichlawson/learn_to_infer

  Learning to Infer project

  Language:Python120

• ReyesDeJong/tf2_vs_pytorch

  Language:Python10