yest/Machine-Learning-Collection

Machine Learning Algorithms and Deep learning in Pytorch

Machine Learning Collection

In this repository I will be uploading everything machine learning related. I try to make the code as clear as possible, and the goal is be to used as a learning resource and a way to lookup problems to solve specific problems. For most I have also done video explanations on youtube to make it easier to follow the code if you're a beginner, and for a few I've done the derivations on my blog. If you got any questions add an issue or would like to add an algorithm do a PR! This repository is contribution friendly 😃

✅ 🔺: Algorithm is tested/untested

Machine Learning

•   Linear Regression - With Gradient Descent ✅
•   Linear Regression - With Normal Equation ✅
•   Logistic Regression
•   Naive Bayes - Gaussian Naive Bayes
•   K-nearest neighbors
•   K-means clustering
•   Support Vector Machine - Using CVXOPT
•   Neural Network

Deeplearning Pytorch

Everything for now is implemented using the PyTorch-framework.

1. Basics

•   Tensor Basics
•   Feedforward Neural Network
•   Convolutional Neural Network
•   Recurrent Neural Network
•   Bidirectional Recurrent Neural Network
•   Loading and saving model
•   Custom Dataset (Images)
•   Transfer Learning and finetuning
•   Transforms & Data Augmentation
•   Learning Rate Scheduler
•   Initialization of weights

2. More Advanced

•   Neural Style Transfer
•   Generative Adversarial Networks
•   Torchtext [1] Torchtext [2] Torchtext [3]
•   Seq2Seq - Sequence to Sequence (LSTM)
•   Seq2Seq + Attention - Sequence to Sequence with Attention (LSTM)
•   Seq2Seq Transformers - Sequence to Sequence with Transformers
•   Transformers from scratch - Attention Is All You Need

3. Architectures

•   LeNet5 - CNN architecture
•   VGG - CNN architecture
•   Inception v1 - CNN architecture
•   ResNet - CNN architecture

4. Utilities

•   TensorBoard Example

5. Smaller Projects

• Exploring MNIST - Needs updating
•   Text Generating LSTM