Neural Breakdown with AVB Courses

Evolution of Transformer Architectures - From Attention to Modern Variants

Explore the evolution of neural attention mechanisms, from basic self-attention to advanced concepts like Multi Query and Grouped Query Attention, with clear visualizations and practical insights.

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

Understanding How Large Language Models Generate Images - From Autoencoders to Multimodal LLMs

Delve into the mechanics of image generation by multimodal LLMs, exploring autoencoders, latent spaces, VQ-VAEs, and codebook embeddings in modern AI models like Gemini and DALL-E.

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

Understanding Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields

Explore the evolution and technical advances of Zip-NeRF, an innovative AI algorithm that revolutionizes 3D scene synthesis from 2D images, building upon NeRF and related technologies.

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

Transformers and the End of Inductive Bias in Neural Networks

Discover how Transformer architectures revolutionized deep learning by breaking away from traditional neural network biases, exploring their data-driven approach versus CNNs and RNNs.

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

Understanding Self-Attention in Transformer Models - Part 2

Dive into the mechanics and power of Self-Attention in Transformer models, exploring its functionality, implementation, and significance in modern deep learning architectures.

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

Understanding Neural Attention in Deep Learning - From Basics to Transformers

Discover how neural attention mechanisms revolutionize AI through visual examples, from basic recommendation systems to advanced transformer architectures and machine translation applications.

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

Four Famous Deep Reinforcement Learning Projects - From Atari to Dactyl

Explore groundbreaking achievements in Deep Reinforcement Learning through four landmark projects: Atari DQN, AlphaGo, DeepMimic, and Dactyl, showcasing AI's evolution in gaming and robotics.

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

Reinforcement Learning vs LLMs in Game-Playing AI - Comparing Methods and Trade-offs

Explore how large language models revolutionize game-playing AI, comparing traditional reinforcement learning with GPT-based approaches in Minecraft and Crafter while examining key papers and methodologies.

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

Natural Language Processing Evolution: From Word2Vec and RNNs to GPT - 50 Key Concepts

Explore the evolution of Natural Language Processing through 50 key concepts, from fundamental Word2Vec and RNNs to modern transformer architectures and GPT models, with clear technical explanations.

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

Segment Anything Model: Architecture, Dataset and Training Breakdown

Dive into the technical architecture, dataset creation, and training methodology behind Meta's Segment Anything Model (SAM) for advanced image segmentation capabilities.

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

The Machine Learning Behind Apple Intelligence - Understanding Modern LLM Architecture

Dive into the technical architecture and algorithmic innovations behind Apple's Foundation Language Models, exploring key concepts from transformer decoders to quantization and reinforcement learning.

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

Segment Anything 2 (SAM 2) - Network Architecture and Implementation Explained

Delve into META AI's SAM-2 technology and understand the advanced network architecture behind promptable visual segmentation for automated object detection in videos.

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

Monocular Depth Estimation: From Neural Networks to Depth Anything V2

Explore modern neural network techniques for estimating depth from 2D images, covering MiDAS, Depth Anything models, and advanced concepts like disparity space and gradient matching loss.

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

Kolmogorov Arnold Networks: An Alternative Paradigm for Deep Learning

Dive into an analysis of Kolmogorov Arnold Networks (KAN), exploring how this innovative approach combines the strengths of MLPs and splines to advance deep learning architecture and capabilities.

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

Coding Image Segmentation with UNet for Football Player Detection

Master image segmentation using UNet architecture, from dataset preparation to implementation in PyTorch, with practical application in football player detection and comprehensive loss function analysis.

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