Stars
ragavvenkatesan / metrics
Forked from Lightning-AI/torchmetricsMachine learning metrics for distributed, scalable PyTorch applications.
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Implementation of our proposed algorithm in domain adaptation for image classification
[ECCV 2018] AMC: AutoML for Model Compression and Acceleration on Mobile Devices
Probabilistic time series modeling in Python
A tool for the dual-head presentation of PDF slides on Mac OS X, most likely using a laptop and a projector. The project arose out of the need to correctly project slides created with LaTeX's beame…
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
Automatically "block" people in images (like Black Mirror) using a pretrained neural network.
Awesome Generative Adversarial Networks with tensorflow
Collection of technical/paper notes related to reinforcement learning, with compact summary and detailed mathematical derivations.
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
An interactive book on deep learning. Much easy, so MXNet. Wow. [Straight Dope is growing up] ---> Much of this content has been incorporated into the new Dive into Deep Learning Book available at …
⏰ AI conference deadline countdowns
Minimal and clean examples of machine learning algorithms implementations
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
LaTeX Template for Typesetting Arizona State University Dissertations and Theses
An adversarial example library for constructing attacks, building defenses, and benchmarking both
Code for the paper "Improved Techniques for Training GANs"
codeaudit / Tensorflow-101
Forked from sjchoi86/Tensorflow-101TensorFlow Tutorials
Awesome paper list with code about generative adversarial nets
Programming exercises for the Stanford Unsupervised Feature Learning and Deep Learning Tutorial
Code for Ragav Venkatesan, Parag Shridhar Chandakkar, Baoxin Li "Simpler non-parametric methods provide as good or better results to multiple-instance learning." at the IEEE International Conferenc…
This toolbox is support material for the book on CNN (http://www.convolution.network).
The most cited deep learning papers
An Open Source Machine Learning Framework for Everyone
A Theano framework for building and training neural networks
Residual networks implementation using Keras-1.0 functional API