Understanding Convolutional Neural Networks | Part 1 / 3 - The Basics

Uniform Manifold Approximation and Projection (UMAP) | Dimensionality Reduction Techniques (5/5)

t-distributed Stochastic Neighbor Embedding (t-SNE) | Dimensionality Reduction Techniques (4/5)

Multidimensional Scaling (MDS) | Dimensionality Reduction Techniques (3/5)

Principal Component Analysis (PCA) | Dimensionality Reduction Techniques (2/5)

Dimensionality Reduction Techniques | Introduction and Manifold Learning (1/5)

LoRA explained (and a bit about precision and quantization)

Vision Transformer Quick Guide - Theory and Code in (almost) 15 min

Personalized Image Generation (using Dreambooth) explained!

Equivariant Neural Networks | Part 3/3 - Transformers and GNNs

Equivariant Neural Networks | Part 2/3 - Generalized CNNs

Equivariant Neural Networks | Part 1/3 - Introduction

State of AI 2022 - My Highlights