Tejas Shahpuri's repositories
Python
All Algorithms implemented in Python
cracking-the-coding-interview
:books: Python and C++ solutions with automated unit tests.
PyTorch
Some ML in PyTorch
GeneratingImageRating
Generating Image Ratings using Neural Networks
Kaggle-CDiscount-Challenge
Kaggle cdiscount image classification challenge
openface
Face recognition with deep neural networks.
PS4-5.01-WebKit-Exploit-PoC
PS4 5.01 WebKit Exploit PoC
NeuralNetworkFIFA18
This is a neural network trained to play Fifa 18
Mask_RCNN
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
deep-q-learning
PyTorch Implementation of Deep Q-Learning with Experience Replay in Atari Game Environments, as made public by Google DeepMind
chatbot-rnn
A toy chatbot powered by deep learning and trained on data from Reddit
darknet
Convolutional Neural Networks
basic-yolo-keras
Easy training on custom dataset. Various backends (MobileNet and SqueezeNet) supported. A YOLO demo to detect raccoon run entirely in brower is accessible at https://git.io/vF7vI (not on Windows).
xsolve-face-validator-bundle
Open-source Symfony3 bundle for validating faces on pictures
Visual-Analytics
Visual Analytics
pygta5
Explorations of Using Python to play Grand Theft Auto 5.
Yelp-on-MongoDB
Yelp on MongoDB
awesome-machine-learning
A curated list of awesome Machine Learning frameworks, libraries and software.
keras
Deep Learning library for Python. Runs on TensorFlow, Theano, or CNTK.
models
Models and examples built with TensorFlow
face-alignment
:fire: 2D and 3D Face alignment library build using pytorch
ctci
Cracking the Coding Interview, 5th Edition
CDiscountKeras
Tweaked version of Human Analog's kernel for use with Keras 2.0.8
keras-inception-resnetV2
Keras implementation of Google's inception-resnet-v2 Architecture
Asynchronous-Methods-for-Deep-Reinforcement-Learning
Using a paper from Google DeepMind I've developed a new version of the DQN using threads exploration instead of memory replay as explain in here: http://arxiv.org/pdf/1602.01783v1.pdf I used the one-step-Q-learning pseudocode, and now we can train the Pong game in less than 20 hours and without any GPU or network distribution.