Advanced Architectures
Explore cutting-edge neural network designs. Study ResNet, attention mechanisms, and architectural innovations.
Prerequisites
Complete Level 4: Computer Vision
🎯What You'll Learn
- ✓Residual networks and skip connections
- ✓Attention mechanisms in depth
- ✓U-Net for image-to-image tasks
- ✓Autoencoders and variational autoencoders
- ✓Neural architecture search
💪Skills You'll Gain
🏆Learning Outcomes
📖Interactive Modules (10)
Attention is All You Need
Deep dive into the seminal Transformer paper and self-attention mechanisms.
Vision Transformer (ViT)
Learn how Transformers are applied to computer vision with Vision Transformer (ViT) architecture.
CLIP Model Explorer
Explore CLIP, connecting vision and language through contrastive learning.
Multi-Modal Learning
Understand models that process multiple data types: text, images, audio simultaneously.
Graph Neural Networks
Learn GNNs for processing graph-structured data like social networks and molecules.
Reinforcement Learning Intro
Introduction to reinforcement learning: agents, rewards, and learning through interaction.
Q-Learning Visualizer
Master Q-learning algorithm, value functions, and model-free reinforcement learning.
Policy Gradient Methods
Understand policy gradient methods for training agents in complex environments.
Actor-Critic Architectures
Learn actor-critic architectures combining value and policy-based reinforcement learning.
AlphaGo Strategy Breakdown
Explore AlphaGo architecture, Monte Carlo tree search, and how AI mastered Go.