All Projects → Wei2624 → AI_Learning_Hub

Wei2624 / AI_Learning_Hub

Licence: MIT license
AI Learning Hub for Machine Learning, Deep Learning, Computer Vision and Statistics

Programming Languages

HTML
75241 projects
ruby
36898 projects - #4 most used programming language

Projects that are alternatives of or similar to AI Learning Hub

MachineLearning
Implementations of machine learning algorithm by Python 3
Stars: ✭ 16 (-69.81%)
Mutual labels:  machine-learning-algorithms, gaussian-mixture-models, perceptron-learning-algorithm
Statistical-Learning-using-R
This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.
Stars: ✭ 27 (-49.06%)
Mutual labels:  machine-learning-algorithms, regularization, svm-classifier
Generative models tutorial with demo
Generative Models Tutorial with Demo: Bayesian Classifier Sampling, Variational Auto Encoder (VAE), Generative Adversial Networks (GANs), Popular GANs Architectures, Auto-Regressive Models, Important Generative Model Papers, Courses, etc..
Stars: ✭ 276 (+420.75%)
Mutual labels:  generative-model, variational-inference
Awesome Vaes
A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.
Stars: ✭ 418 (+688.68%)
Mutual labels:  generative-model, variational-inference
bitcoin-prediction
bitcoin prediction algorithms
Stars: ✭ 21 (-60.38%)
Mutual labels:  machine-learning-algorithms, svm-classifier
Deep Generative Models For Natural Language Processing
DGMs for NLP. A roadmap.
Stars: ✭ 185 (+249.06%)
Mutual labels:  generative-model, variational-inference
Generalized-PixelVAE
PixelVAE with or without regularization
Stars: ✭ 64 (+20.75%)
Mutual labels:  generative-model, variational-inference
Variational Ladder Autoencoder
Implementation of VLAE
Stars: ✭ 196 (+269.81%)
Mutual labels:  generative-model, variational-inference
adaptive-f-divergence
A tensorflow implementation of the NIPS 2018 paper "Variational Inference with Tail-adaptive f-Divergence"
Stars: ✭ 20 (-62.26%)
Mutual labels:  generative-model, variational-inference
Handwritten-Digits-Classification-Using-KNN-Multiclass Perceptron-SVM
🏆 A Comparative Study on Handwritten Digits Recognition using Classifiers like K-Nearest Neighbours (K-NN), Multiclass Perceptron/Artificial Neural Network (ANN) and Support Vector Machine (SVM) discussing the pros and cons of each algorithm and providing the comparison results in terms of accuracy and efficiecy of each algorithm.
Stars: ✭ 42 (-20.75%)
Mutual labels:  machine-learning-algorithms, perceptron-learning-algorithm
artificial neural networks
A collection of Methods and Models for various architectures of Artificial Neural Networks
Stars: ✭ 40 (-24.53%)
Mutual labels:  machine-learning-algorithms, variational-inference
PyLDA
A Latent Dirichlet Allocation implementation in Python.
Stars: ✭ 51 (-3.77%)
Mutual labels:  machine-learning-algorithms, variational-inference
car-OCR
基于机器学习和OCR的车牌识别系统 @fujunhao
Stars: ✭ 39 (-26.42%)
Mutual labels:  machine-learning-algorithms, svm-classifier
MachineLearningSeries
Vídeos e códigos do Universo Discreto ensinando o fundamental de Machine Learning em Python. Para mais detalhes, acompanhar a playlist listada.
Stars: ✭ 20 (-62.26%)
Mutual labels:  machine-learning-algorithms, k-means-clustering
latent-pose-reenactment
The authors' implementation of the "Neural Head Reenactment with Latent Pose Descriptors" (CVPR 2020) paper.
Stars: ✭ 132 (+149.06%)
Mutual labels:  generative-model
pytorch-GAN
My pytorch implementation for GAN
Stars: ✭ 12 (-77.36%)
Mutual labels:  generative-model
GDLibrary
Matlab library for gradient descent algorithms: Version 1.0.1
Stars: ✭ 50 (-5.66%)
Mutual labels:  machine-learning-algorithms
Cross-Speaker-Emotion-Transfer
PyTorch Implementation of ByteDance's Cross-speaker Emotion Transfer Based on Speaker Condition Layer Normalization and Semi-Supervised Training in Text-To-Speech
Stars: ✭ 107 (+101.89%)
Mutual labels:  generative-model
Moo-GBT
Library for Multi-objective optimization in Gradient Boosted Trees
Stars: ✭ 63 (+18.87%)
Mutual labels:  machine-learning-algorithms
denoising-diffusion-pytorch
Implementation of Denoising Diffusion Probabilistic Model in Pytorch
Stars: ✭ 2,313 (+4264.15%)
Mutual labels:  generative-model

AI Learning Hub

LICENSE





Photo Credit: Liam Kay

AI Learning Hub is an open-sourced machine learning handbook. We contribute to this repo by summarizing interesting blog, course and/or notes of machine learning, deep learning, computer vision, robotics and/or statistics. We also intend to provide each post with Chinese version.

We do this because we love AI and sharing. Excellent materials are the step stone for learning AI. We think everyone is deserved a chance to study AI with excellent materials. We welcome anyone to join us to make it better!

And you own whatever you write here!

What notes are/can be posted here?

We are looking for any related notes that are genuinely created by your own. By genuinity, we mean one of the following:

  1. You create and write the contents of notes from scratch. Everything is original.

  2. You summarize contents from related course(s), book(s) and note(s). You can merge contents from multiple sources. Although this is expected to be a summary, your summary should be original.

  3. You translate one of the notes in THIS repo.

View Contents

We provide with two ways to view and learn the blogs.

View author's homepage (Highly Recommended!)

The best way to view the contents of any blog is to view the homepage of the author of that blog that especially interests you. The information of author's homepage of each blog is listed in this README and will be updated as any changes happen.

We highly recommend this way to view the contents of any blog.

Use Jekyll and Ruby to view locally (Not Recommended)

  1. Install Ruby environment. Instructions can be found here.

  2. Run

gem install jekyll bundler
  1. Run
git clone https://github.com/Wei2624/AI_Learning_Hub.git
cd AI_Learning_Hub
bundle install
bundle exec jekyll build
  1. In _site directory, you can find .html file. Then, you are able to view them locally.

Join us

You are very welcome to join us to improve this repo more!

Write Blog

The easiest way to contribute is to fork this project and write your own contents. Remember that you own whatever you write.

To unify the style of each blog, you should use markdown as the syntax with mathjax as a plugin for math. Of course, you can insert html code whenever you want. An example of header of a blog can be as below:

---
layout: single
mathjax: true
title: Regularization and Model Selection
share: true
permalink: /MachineLearning/sv_regularization_model_selection/
---

For layout, you better either choose single where comments are enabled or archive where comments are disabled. For more layout options, you can view here.

permalink is a slef-defined relative url path. If you want to host up your blog, you can append permalink to your site-url.

You better follow this procedure so that people can run ruby command to generate local page for view.

Host Blog

You can put up your own blog. The easiest way to do this is to use submodule from git.

Essentially, you have your own repo. Then you can run git submodule command to add this repo as a subdirectory to your original repo. This repo will just become one of the folders in your repo. You can access whatever you write here.

Distribution of contents

Distribution of contents without author's permission is strictly prohibited.

Please respect the authorship of each blog there. If you want to distribute them, you can ask the author for permission. Every author here has all the rights to their written blog and is fully responsible for their written blogs.

Blog Information

Blogs in English

Module Blog Title Lang Author Contact
ML Generative Algorithm EN Wei Zhang [email protected]
ML Discriminative Algorithm EN Wei Zhang [email protected]
ML Support Vector Machine EN Wei Zhang [email protected]
ML Bias-Varaince and Error Analysis EN Wei Zhang [email protected]
ML Learning Theory EN Wei Zhang [email protected]
ML Regularization and Model Selection EN Wei Zhang [email protected]
ML Online Learning and Perceptron Algorithm EN Wei Zhang [email protected]
ML K-Means EN Wei Zhang [email protected]
ML EM Algorithm EN Wei Zhang [email protected]
ML Variational Inference EN Wei Zhang [email protected]
DL Nerual Networks EN Wei Zhang [email protected]
DL Backpropagation EN Wei Zhang [email protected]

Blogs in Chinese

Module Blog Title Lang Author Contact
ML Generative Algorithm CH Zishi Yan WeChat:air-sowhat
ML Discriminative Algorithm CH Xiaoxiao Lei WeChat: Dark417
ML Support Vector Machine CH Zishi Yan WeChat:air-sowhat
ML Bias-Varaince and Error Analysis CH Xiaoxiao Lei WeChat: Dark417
ML Regularization and Model Selection CH Xiaoxiao Lei WeChat: Dark417
Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].