Personal re-implementations of known Machine Learning architectures, layers, algorithms and more. Re-implementations might be simplified and approximate. The goal is learning / familiarizing / practicing with the core concepts 🙂.
Implementation of the "Denoising Diffusion Probabilistic Models" paper. I use MNIST and FashionMNIST dataset as toy examples. The model used is a custom U-Net like architecture with the use of positional embeddings. Pre-trained models for both datasets (20 epochs only) are provided in the when using Git Large File System. Check out the Blog for a step-by-step explanation.
Implementation of the "Density estimation using Real NVP" paper. I re-implement and use 30 Affine Coupling layers to create a normalizing flow that can generate MNIST digits. The generated digits come with associated log probabilities, which tell which images are the most likely according to the model. Here's a glance at the (not so impeccable) final result:
Implementation of the famous "Proximal Policy Optimization Algorithms" paper. I implement the simple PPO algorithm from scratch in pytorch using weights & biases for logging the loss terms and the average reward through iterations.
Implementation of the "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" paper. The MNIST dataset is used as a toy example for classification task. Blog .