What is the Transformers' Context Window in Deep Learning and How to Make it Long

Explore the concept of context windows in transformer architecture and learn techniques to extend them in this 27-minute video tutorial. Discover why larger context is beneficial through examples like retrieval tests, needle-in-a-haystack challenges, and book translation tasks. Review attention calculation fundamentals and position encoding before diving into the challenges of increasing context length. Master various strategies for extending context windows including improved positional encodings, optimized attention calculations with Flash Attention, sparse attention mechanisms, low-rank decomposition, chunking, and linear component approaches. Examine real-world implementations in cutting-edge models like LLama 4 (with its 10M token context) and Gemini 2.5 (1M tokens). The tutorial includes comprehensive references to research papers and additional resources for deeper understanding of long-context transformer models.