Канал: DeepFindr

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

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

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

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

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

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

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

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

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

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

LoRA explained (and a bit about precision and quantization)

LoRA explained (and a bit about precision and quantization)

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

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

Personalized Image Generation (using Dreambooth) explained!

Personalized Image Generation (using Dreambooth) explained!

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

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

Equivariant Neural Networks | Part 2/3 - Generalized CNNs

Equivariant Neural Networks | Part 2/3 - Generalized CNNs

Equivariant Neural Networks | Part 1/3 - Introduction

Equivariant Neural Networks | Part 1/3 - Introduction

State of AI 2022 - My Highlights

State of AI 2022 - My Highlights

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

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

Contrastive Learning in PyTorch - Part 1: Introduction

Contrastive Learning in PyTorch - Part 1: Introduction

Self-/Unsupervised GNN Training

Self-/Unsupervised GNN Training

Diffusion models from scratch in PyTorch

Diffusion models from scratch in PyTorch

Causality and (Graph) Neural Networks

Causality and (Graph) Neural Networks

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

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

Converting a Tabular Dataset to a Temporal Graph Dataset for GNNs

Converting a Tabular Dataset to a Temporal Graph Dataset for GNNs

Converting a Tabular Dataset to a Graph Dataset for GNNs

Converting a Tabular Dataset to a Graph Dataset for GNNs

How to handle Uncertainty in Deep Learning #2.2

How to handle Uncertainty in Deep Learning #2.2

How to handle Uncertainty in Deep Learning #2.1

How to handle Uncertainty in Deep Learning #2.1

How to handle Uncertainty in Deep Learning #1.2

How to handle Uncertainty in Deep Learning #1.2

How to handle Uncertainty in Deep Learning #1.1

How to handle Uncertainty in Deep Learning #1.1

Recommender Systems using Graph Neural Networks

Recommender Systems using Graph Neural Networks

Fake News Detection using Graphs with Pytorch Geometric

Fake News Detection using Graphs with Pytorch Geometric

Fraud Detection with Graph Neural Networks

Fraud Detection with Graph Neural Networks

Traffic Forecasting with Pytorch Geometric Temporal

Traffic Forecasting with Pytorch Geometric Temporal

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

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

Python Graph Neural Network Libraries (an Overview)

Python Graph Neural Network Libraries (an Overview)

Explaining Twitch Predictions with GNNExplainer

Explaining Twitch Predictions with GNNExplainer

How to explain Graph Neural Networks (with XAI)

How to explain Graph Neural Networks (with XAI)

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

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

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

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

GNN Project #4.3 - Code explanation

GNN Project #4.3 - Code explanation

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

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

GNN Project #4.2 - GVAE Training and Adjacency reconstruction

GNN Project #4.2 - GVAE Training and Adjacency reconstruction

GNN Project #4.1 - Graph Variational Autoencoders

GNN Project #4.1 - Graph Variational Autoencoders

GNN Project #3.2 - Graph Transformer

GNN Project #3.2 - Graph Transformer

GNN Project #3.1 - Graph-level predictions

GNN Project #3.1 - Graph-level predictions

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

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

GNN Project #1 - Introduction to HIV dataset

GNN Project #1 - Introduction to HIV dataset

Understanding Graph Attention Networks

Understanding Graph Attention Networks

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

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

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

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

Explainable AI explained! | #4 SHAP

Explainable AI explained! | #4 SHAP

Explainable AI explained! | #3 LIME

Explainable AI explained! | #3 LIME

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

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

Explainable AI explained! | #1 Introduction

Explainable AI explained! | #1 Introduction

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

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

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

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

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

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

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

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

Node Classification on Knowledge Graphs using PyTorch Geometric

Node Classification on Knowledge Graphs using PyTorch Geometric

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

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

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

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

Understanding Graph Neural Networks | Part 1/3 - Introduction

Understanding Graph Neural Networks | Part 1/3 - Introduction