kvaidya005 Goto Github PK

2015

Public material for CS109

awesome-imitation-learning

A curated list of awesome imitation learning resources and publications

awesome-learning-with-label-noise

A curated list of resources for Learning with Noisy Labels

awesome-self-supervised-learning

A curated list of awesome self-supervised methods

backdoor

Code implementation of the paper "Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks", at IEEE Security and Privacy 2019.

cadl

Course materials/Homework materials for the FREE MOOC course on "Creative Applications of Deep Learning w/ Tensorflow" #CADL

char-rnn

Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch

cond_rnn

Conditional RNNs made easy with Tensorflow and Keras.

d2l-en

Interactive deep learning book with code, math, and discussions. Available in multi-frameworks.

deep-learning-drizzle

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

deep-learning-illustrated

Deep Learning Illustrated (2020)

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

deeplearning4j-examples

Deeplearning4j Examples (DL4J, DL4J Spark, DataVec)

deeplearning_101

dltfpt

Deep Learning with TensorFlow, Keras, and PyTorch

duke-tsinghua-mlss-2017

Duke-Tsinghua Machine Learning Summer School 2017

fastbook

The fastai book, published as Jupyter Notebooks

finite_mdp

Solve finite Markov Decision Process models.

ganhacks

starter from "How to Train a GAN?" at NIPS2016

gans

Generative Adversarial Networks implemented in PyTorch and Tensorflow

git-and-github

An introduction to git.

gt-nlp-class

Course materials for Georgia Tech CS 4650 and 7650, "Natural Language"

handson-ml

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

introtodeeplearning

Lab Materials for MIT 6.S191: Introduction to Deep Learning

keras-attention

Visualizing RNNs using the attention mechanism

keras-attention-mechanism

Attention mechanism Implementation for Keras.

lstm-bus-route-prediction

The project uses Keras on top of a Tensorflow LSTM to predict bearings, latitude/longitude, delays, and speeds for Metlink bus ping data

machine-learning-interview-questions

This repository is to prepare for Machine Learning interviews.

machine_learning_examples

A collection of machine learning examples and tutorials.