Aleksa Gordić - The AI Epiphany Courses

ETA Prediction with Graph Neural Networks in Google Maps - Paper Explained

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Explore Graph Neural Networks for ETA prediction in Google Maps, covering graph formation, features, GNN architecture, loss functions, baselines, inference, and experimental results.

• YouTube
• 39 minutes
• On-Demand
• Free Video

Implementing DeepMind's DQN from Scratch - Project Update

Explore DeepMind's DQN implementation process, including project organization, code walkthrough, and visualization techniques for reinforcement learning algorithms.

• YouTube
• 27 minutes
• On-Demand
• Free Video

EfficientNetV2 - Smaller Models and Faster Training - Paper Explained

Explore EfficientNetV2's improved image classification performance, including progressive training, Fused-MBConv layer, and novel reward function for Neural Architecture Search.

• YouTube
• 28 minutes
• On-Demand
• Free Video

MuZero - Mastering Atari, Go, Chess, and Shogi by Planning with a Learned Model - RL Paper Explained

Explore MuZero, an AI agent mastering games without rules. Learn its innovative approach to planning with a learned model, achieving state-of-the-art results in Go, Chess, Shogi, and Atari.

• YouTube
• 28 minutes
• On-Demand
• Free Video

OpenAI - Solving Rubik's Cube with a Robot Hand - RL Paper Explained

Detailed explanation of OpenAI's robotic hand solving Rubik's Cube using simulation-based training, covering system design, domain randomization, and reinforcement learning techniques.

• YouTube
• 55 minutes
• On-Demand
• Free Video

DeepMind's AlphaGo Zero and AlphaZero - RL Paper Explained

Explore DeepMind's revolutionary AI agents that mastered Go, Chess, and Shogi through self-play, without human knowledge. Learn about their inner workings, training process, and groundbreaking results.

• YouTube
• 42 minutes
• On-Demand
• Free Video

AlphaGo - Mastering the Game of Go with Deep Neural Networks and Tree Search - RL Paper Explained

Comprehensive exploration of AlphaGo's groundbreaking AI system, detailing its neural networks, search algorithms, and innovative techniques that led to defeating professional Go players.

• YouTube
• 51 minutes
• On-Demand
• Free Video

DQN - Playing Atari with Deep Reinforcement Learning - RL Paper Explained

Explore deep reinforcement learning with DQN, covering experience replay, MDP formalism, function approximators, and its application to Atari games. Gain insights into RL challenges and solutions.

• YouTube
• 51 minutes
• On-Demand
• Free Video

How to Get Started With Graph ML - Blog Walkthrough

Comprehensive introduction to Graph Machine Learning, covering applications, methods, and resources. Explores research challenges, GNN expressivity, and related subfields, providing a solid foundation for beginners.

• YouTube
• 47 minutes
• On-Demand
• Free Video

Graph Attention Network Project Walkthrough

Comprehensive walkthrough of Graph Attention Network implementation, covering dataset analysis, key implementation details, and related deep learning projects for enthusiasts and practitioners.

• YouTube
• 40 minutes
• On-Demand
• Free Video

Temporal Graph Networks - GNN Paper Explained

Explore temporal graph networks and dynamic graphs, learning advanced techniques for graph machine learning, including time-based sampling, memory management, and attention mechanisms.

• YouTube
• 39 minutes
• On-Demand
• Free Video

OpenAI CLIP - Connecting Text and Images - Paper Explained

Explore OpenAI's CLIP model for connecting text and images, covering its methodology, zero-shot capabilities, and performance comparisons. Learn about its embedding space quality and limitations.

• YouTube
• 53 minutes
• On-Demand
• Free Video

Graph SAGE - Inductive Representation Learning on Large Graphs - GNN Paper Explained

Deep dive into Graph SAGE, exploring its innovative approach to large-scale graph learning. Covers key concepts, training methods, aggregator functions, and comparisons with other graph neural networks.

• YouTube
• 44 minutes
• On-Demand
• Free Video

Graph Convolutional Networks - GNN Paper Explained

Comprehensive exploration of Graph Convolutional Networks, covering theory, implementation, and applications. Delves into spectral methods, Weisfeiler-Lehman perspective, and GNN depth, offering insights for both beginners and experts.

• YouTube
• 50 minutes
• On-Demand
• Free Video

Graph Attention Networks - GNN Paper Explained

Explore Graph Attention Networks (GAT) in-depth, covering graph theory basics, GAT methodology, multi-head versions, visualizations, and applications in transductive/inductive learning scenarios.

• YouTube
• 38 minutes
• On-Demand
• Free Video