A curated series of PyTorch notebooks exploring core deep learning architectures (from MLPs and CNNs to attention and GANs). Intended as a reference and self-study repository.

A simple, easy-to-understand library for diffusion models using Flax and Jax. Includes detailed notebooks on DDPM, DDIM, and EDM with simplified mathematical explanations. Made as part of my journey for learning and experimenting with generative AI.

This notebook implements a basic attention mechanism to demonstrate its functionality using a simple RGB matrix as an input. It includes steps for creating random matrices for RGB channels, computing attention weights, and applying the attention mechanism in a simulated environment.

Exercise notebooks for CVND, the Udacity Computer Vision Nanodegree.

Deep learning research implemented on notebooks using PyTorch.

Assignments and lab notebooks of NLP Specialization by DeepLearning.ai

The notebook explains the various steps to obtain the results of publication: "Is Space-Time Attention All You Need for Video Understanding?"

A small, interpretable codebase containing the re-implementation of a few "deep" NLP models in PyTorch. Colab notebooks to run with GPUs. Models: word2vec, CNNs, transformer, gpt.

Notes on ML and DL with jupyter notebooks (python)

Snippets of nlp frameworks

In this notebook, we look at how attention is implemented. We will focus on implementing attention in isolation from a larger mode

NLP学习笔记的Notebook，包含经典模型的理解与相关实践。

The basic notebook fot implementing the attention.

Notebooks for running and visualizing results using trained models for linguistic complexity.