In this repository, I will keep some notes I have taken while going through the Dive into Deep Learning (d2ai) book (https://d2l.ai/), as well as some google colab notebooks I coded while going through the examples in the book, coded in Pytorch, for learning purposes. That is, not using the d2ai package provided by the authors.
- 📊 Chapter 3 Notebook: Linear regression with simulated data
- 👕 Chapter 4 Notebook: Softmax regression with the Fashion-MNIST dataset
- 🏠 Chapter 5 Notebook: Multilayer perceptron for Kaggle House Price dataset
- 👚 Chapter 6 Notebook: LeNet with the Fashion-MNIST dataset
- 🎽 Chapter 7 Notebook: AlexNet, VGG, NiN, GoogLeNet, LeNet with Batch Normalization, ResNet and DenseNet implementations with the Fashion-MNIST Dataset.
- 📘Chapter 8 Notebook: Dealing with text data (corpus + tokenization) and using basic RNNs for character prediction (Char RNNs).
You can either visualize the notebooks here on Github, and run them in Google Colab or locally.