Vaibhav21pandit

Vaibhav21pandit's Stars

• Consensys/smashing-smart-contracts-workshop

  Language:Solidity2010

• PacktPublishing/Machine-Learning-for-Algorithmic-Trading-Second-Edition_Original

  Machine Learning for Algorithmic Trading, Second Edition - published by Packt

  Language:Jupyter Notebook1.3k498

• chaincodelabs/bitcoin-curriculum

  Bitcoin Protocol Development Curriculum

  649121

• EleutherAI/gpt-neo

  An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.

  Language:Python8.3k955

• ttavni/2D_Text_Clustering

  Using word embeddings, TFIDF and text-hashing to cluster and visualise text documents

  Language:Python155

• ajcr/100-pandas-puzzles

  100 data puzzles for pandas, ranging from short and simple to super tricky (60% complete)

  Language:Jupyter Notebook2.6k3.1k

• jiadaizhao/Advanced-Machine-Learning-Specialization

  Advanced Machine Learning Specialization on Coursera

  Language:Jupyter Notebook221223

• wesm/pydata-book

  Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media

  Language:Jupyter Notebook22.5k15.3k

• mit-han-lab/once-for-all

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

  Language:Python1.9k334

• facebookresearch/detr

  End-to-End Object Detection with Transformers

  Language:Python13.8k2.5k

• zcakhaa/DeepLOB-Deep-Convolutional-Neural-Networks-for-Limit-Order-Books

  This jupyter notebook is used to demonstrate our recent work, "DeepLOB: Deep Convolutional Neural Networks for Limit Order Books", published in IEEE Transactions on Singal Processing. We use FI-2010 dataset and present how model architecture is constructed here. The FI-2010 is publicly avilable and interested readers can check out their paper.

  Language:Jupyter Notebook425219

• Cyash/Pothole_Detection

  Pothole Detection using Masked-RCNN

  Language:Python8521

• sadighian/recommendation-gym

  MovieLens recommendation system using reinforcement learning (GYM + PPO)

  Language:Python4611

• pythonlessons/RL-Bitcoin-trading-bot

  Trying to create Reinforcement Learning powered Bitcoin trading bot

  Language:Python389204

• sadighian/crypto-rl

  Deep Reinforcement Learning toolkit: record and replay cryptocurrency limit order book data & train a DDQN agent

  Language:Python866237

• TaylorFacen/nomics-python

  A Python wrapper for the Nomics API

  Language:Python5214

• caprover/caprover

  Scalable PaaS (automated Docker+nginx) - aka Heroku on Steroids

  Language:TypeScript13.5k862

• lucidrains/deep-daze

  Simple command line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). Technique was originally created by https://twitter.com/advadnoun

  Language:Python4.4k324

• nandorojo/dripsy

  🍷 Responsive, unstyled UI primitives for React Native + Web.

  Language:TypeScript2.1k82

• sorich87/bootstrap-tour

  Quick and easy product tours with Twitter Bootstrap Popovers

  Language:CoffeeScript4.4k942

• lucidrains/DALLE-pytorch

  Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch

  Language:Python5.6k642

• librosa/librosa

  Python library for audio and music analysis

  Language:Python7.3k971

• abhisekpadhi/livspace

  Recommender system with REST API, created for livspace hackathon.

  Language:Python33

• jbhuang0604/awesome-computer-vision

  A curated list of awesome computer vision resources

  21.2k4.2k

• sgrvinod/a-PyTorch-Tutorial-to-Image-Captioning

  Show, Attend, and Tell | a PyTorch Tutorial to Image Captioning

  Language:Python2.8k718

• mesmith027/streamlit-cheat-sheet

  A cheat sheet for streamlit

  1

• alankbi/detecto

  Build fully-functioning computer vision models with PyTorch

  Language:Python616103

• typicode/react-fake-props

  🔮 Magically generate fake props for your React tests

  Language:JavaScript62723

• dangkhoasdc/awesome-ai-residency

  List of AI Residency Programs

  3.1k269

• arxiv-vanity/arxiv-vanity

  Renders papers from arXiv as responsive web pages so you don't have to squint at a PDF.

  Language:Python1.6k94