Machine Learning Tutorials for PyTorch
This is a series of machine learning tutorials I'm writing for implementing deep learning models on your own with the amazing PyTorch library. Basic knowledge of PyTorch and neural networks is assumed. These tutorials are composed of two sections:
This section is based on research papers. It is designed to enable coding while reading the paper, and executed it in a Colab for both coding and running small tasks. This write-up serves as a review, blending personal thoughts on the paper with key insights.
This section entails various tasks implemented in Colab, serving as an introduction to fundamental machine learning concepts.
| Category | Tutorial | KeyWord |
|---|---|---|
| Object Detection | Faster R-CNN(2) | Faster R-CNN |
| Object Detection | Faster R-CNN(1) | Faster R-CNN |
| NLP | Seq2Seq | Machine Translation |
| NLP | Simple Word Window Classification | |
| Computer Vision | ResNet | Residual Neural Network |
| Computer Vision | AutoEncoder | AutoEncoder, Denoising AutoEncoder |
| ML | Recurrent Neural Networks | RNN, LSTM |
| ML | Simple CNN | Convolutional Neural Network |
| ML | Fully-Connected Neural Networks | |
| Basic | NLP Basic Tutorial | Natural Language Processing |
| Basic | Losses | Multi-Class SVM, Softmax, Cross-Entropy |
| Basic | k-Nearest Neighbors | k-Nearest Neighbors, Cross-Validation |
| Basic | Pytorch Basic | PyTorch |
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