28:55

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

31:20

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

30:12

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

26:17

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

13:10

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

17:07

LoRA explained (and a bit about precision and quantization)

16:51

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

17:45

Personalized Image Generation (using Dreambooth) explained!

16:19

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

15:57

Equivariant Neural Networks | Part 2/3 - Generalized CNNs

18:51

Equivariant Neural Networks | Part 1/3 - Introduction

09:33

State of AI 2022 - My Highlights

27:20

Contrastive Learning in PyTorch - Part 2: CL on Point Clouds

14:21

Contrastive Learning in PyTorch - Part 1: Introduction

12:09

Self-/Unsupervised GNN Training

30:54

Diffusion models from scratch in PyTorch

16:13

Causality and (Graph) Neural Networks

14:15

How to get started with Data Science (Career tracks and advice)

18:11

Converting a Tabular Dataset to a Temporal Graph Dataset for GNNs

15:22

Converting a Tabular Dataset to a Graph Dataset for GNNs

13:40

How to handle Uncertainty in Deep Learning #2.2

13:55

How to handle Uncertainty in Deep Learning #2.1

14:55

How to handle Uncertainty in Deep Learning #1.2

18:38

How to handle Uncertainty in Deep Learning #1.1

13:01

Recommender Systems using Graph Neural Networks

12:38

Fake News Detection using Graphs with Pytorch Geometric

12:16

Fraud Detection with Graph Neural Networks

16:28

Traffic Forecasting with Pytorch Geometric Temporal

14:26

Friendly Introduction to Temporal Graph Neural Networks (and some Traffic Forecasting)

11:09

Python Graph Neural Network Libraries (an Overview)

12:06

Explaining Twitch Predictions with GNNExplainer

15:07

How to explain Graph Neural Networks (with XAI)

16:58

Machine Learning Model Deployment with Python (Streamlit + MLflow) | Part 2/2

10:01

Machine Learning Model Deployment with Python (Streamlit + MLflow) | Part 1/2

18:36

GNN Project #4.3 - Code explanation

15:54

GNN Project #4.3 - One-shot molecule generation - Part 1

27:09

GNN Project #4.2 - GVAE Training and Adjacency reconstruction

12:53

GNN Project #4.1 - Graph Variational Autoencoders

14:03

GNN Project #3.2 - Graph Transformer

18:34

GNN Project #3.1 - Graph-level predictions

15:01

GNN Project #2 - Creating a Custom Dataset in Pytorch Geometric

03:09

GNN Project #1 - Introduction to HIV dataset

15:00

Understanding Graph Attention Networks

28:28

Explainable AI explained! | #6 Layerwise Relevance Propagation with MRI data

16:57

Explainable AI explained! | #5 Counterfactual explanations and adversarial attacks

15:50

Explainable AI explained! | #4 SHAP

13:59

Explainable AI explained! | #3 LIME

20:54

Explainable AI explained! | #2 By-design interpretable models with Microsofts InterpretML

06:53

Explainable AI explained! | #1 Introduction

16:48

How to use edge features in Graph Neural Networks (and PyTorch Geometric)

25:33

Understanding Convolutional Neural Networks | Part 3 / 3 - Transfer Learning and Explainable AI

29:15

Understanding Convolutional Neural Networks | Part 2 / 3 - Wonders of the world CNN with PyTorch

19:59

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

18:51

Node Classification on Knowledge Graphs using PyTorch Geometric

18:41

Understanding Graph Neural Networks | Part 3/3 - Pytorch Geometric and Molecule Data using RDKit

09:26

Understanding Graph Neural Networks | Part 2/3 - GNNs and it's Variants

08:18

Understanding Graph Neural Networks | Part 1/3 - Introduction