Deep Dive Into Self-Attention & Modern AI. Understand transformer architecture from first principles and how LLMs work internally.
Master the architecture that powers modern AI
Learn how transformers compute attention weights and why self-attention revolutionized NLP
Master the full architecture: embeddings, multi-head attention, positional encoding, feed-forward networks
Deep dive into how transformers generate text: tokens, embeddings, logits, sampling strategies
Matrix operations, dot products, derivatives, gradients (or willingness to learn)
Comfortable with Python, NumPy, and ideally some PyTorch basics
Basic NLP understanding: tokenization, embeddings, sequence models (RNNs helpful)
From attention mechanisms to LLM internals
Understand RNN limitations and why transformers were invented. Learn about the vanishing gradient problem and sequence processing challenges.
Start LearningThe original transformer paper explained. Learn how attention allows parallel processing and long-range dependencies.
Start LearningDeep dive into scaled dot-product attention. Understand queries, keys, values, and why scaling matters mathematically.
Start LearningLearn multi-head attention for diverse representations. Master positional encoding strategies (absolute, relative, rotary).
Start LearningBuild the full transformer: embeddings, encoder layers, decoder layers, feed-forward networks, layer normalization.
Start LearningHow LLMs generate text. Understand autoregressive generation, temperature, top-k sampling, and beam search.
Start LearningComplete journey: tokenization → embeddings → transformer blocks → attention → logits → sampling. Practical implementations.
Start LearningApply your understanding with practical implementations
Implement a complete transformer encoder-decoder in PyTorch, train on machine translation task
Start Project →Build a GPT-style decoder-only transformer and train it to generate text character-by-character
Start Project →Analyze what different attention heads learn and visualize attention weights on real text
Start Project →Explore more courses to expand your AI and programming skills
Master practical LLM usage: fine-tuning, inference optimization, building applications
Explore Course →Neural networks, CNNs, RNNs, and attention mechanisms from first principles
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