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mixnmatch

Pytorch implementation of MixNMatch

ml-andrew-ng-code

سورس کد وب سایت یادگیری ماشین دانشگاه استنفورد به فارسی برای مشارکت

ml-beginning

Some early projects on deep learning done as a part of Jeremy Howard's Fastai course

ml-coursera-python-assignments

Python assignments for the machine learning class by andrew ng on coursera with complete submission for grading capability and re-written instructions.

ml-desnapify

Deep convolutional generative adversarial network (DCGAN) trained to remove Snapchat filters from selfie images

ml-engineer-roadmap

WIP: Roadmap to becoming a machine learning engineer in 2020

ml-exercises

Personal exercises from "Hands-On Machine Learning with Scikit-Learn and TensorFlow" from Aurélien Géron

ml-for-beginners

12 weeks, 25 lessons, 50 quizzes, classic Machine Learning for all

ml-from-scratch

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

ml-hypersim

Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding

ml-playground

Playground for ML tasks from "grokking Deep Learning" by Andrew W. Trask book.

ml-practice

All materials from Hands-On Machine Learning by Aurelien Geron

ml-projects

The projects were part of the Udemy course "Machine Learning A-Z: Hands on Python in Data Science" from Kirill Eremenko & Hadelin de Ponteves.

ml-projects-1

ml-scikit-keras-tensorflow

Reading "Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron

ml-visual-recognition

Large Scale Visual Object Recognition

ml-youtube-courses

📺 A place to discover the latest machine learning courses on YouTube.

ml_recommendation_system

ml_tutorial

This repository explains machine learning concepts

ml_with_python-study_notes

These are study notes from the book Introduction to Machine Learning with Python, by Andreas C. Müller and Sarah Guido.

mlbook

Exercises on Machine Learning from Aurelien Geron book

mlfromscratch

Machine Learning algorithm implementations from scratch.

mlt_starterkit

mmdetection

OpenMMLab Detection Toolbox and Benchmark

mnist-cnn

Convolutional neural networks with Python 3

mnist-handwritten-digit-recognition

mnist-signs-dataset

mnist_gan

In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits! GANs were first reported on in 2014 from Ian Goodfellow and others in Yoshua Bengio's lab. Since then, GANs have exploded in popularity. Here are a few examples to check out: Pix2Pix CycleGAN & Pix2Pix in PyTorch, Jun-Yan Zhu A list of generative models The idea behind GANs is that you have two networks, a generator 𝐺 and a discriminator 𝐷 , competing against each other. The generator makes "fake" data to pass to the discriminator. The discriminator also sees real training data and predicts if the data it's received is real or fake. The generator is trained to fool the discriminator, it wants to output data that looks as close as possible to real, training data. The discriminator is a classifier that is trained to figure out which data is real and which is fake. What ends up happening is that the generator learns to make data that is indistinguishable from real data to the discriminator. The general structure of a GAN is shown in the diagram above, using MNIST images as data. The latent sample is a random vector that the generator uses to construct its fake images. This is often called a latent vector and that vector space is called latent space. As the generator trains, it figures out how to map latent vectors to recognizable images that can fool the discriminator. If you're interested in generating only new images, you can throw out the discriminator after training. In this notebook, I'll show you how to define and train these adversarial networks in PyTorch and generate new images!

model_search

models

A collection of pre-trained, state-of-the-art models in the ONNX format