Follow me on instagram: https://www.instagram.com/unlearn_with_ajmal/
Join my Whatsapp Community: https://tinyurl.com/Whatsapp-UnlearnWithAjmal
Join XandY Career Updates WhatsApp Community: https://chat.whatsapp.com/KZLT6Eb4lw381k6ztIbOrc
Follow me on Facebook: https://www.facebook.com/UnlearnWithAjmal/
In this video, we dive deep into the technical and mathematical foundations of Large Language Models (LLMs)—specifically the Transformer architecture introduced by Vaswani et al. in the groundbreaking paper “Attention Is All You Need.” We explore how Transformers revolutionized natural language processing by using self-attention to capture long-range dependencies more efficiently than previous recurrent or convolutional models.
What You’ll Learn
Tokenization & Embeddings
How text is split into tokens and mapped to dense vector representations (embeddings).
Importance of tokenization algorithms (e.g., Byte-Pair Encoding, WordPiece).
Positional Encoding
Why Transformers need positional encodings to keep track of sequence order.
The sine/cosine formulation and its mathematical rationale.
Self-Attention & Multi-Head Attention
Key, Query, and Value matrices and how dot products reveal token relevance.
Why multiple attention “heads” allow the model to focus on different aspects of the sequence simultaneously.
Feedforward Network & Residual Connections
How pointwise feedforward layers transform attended information.
The role of skip connections and normalization layers in stabilizing training.
Training Objectives
Common objectives like masked language modeling (BERT) and next-token prediction (GPT).
The role of large-scale datasets in learning language patterns and representations.
Why Transformers Matter
How they power ChatGPT, GPT-3, GPT-4, BERT, and other state-of-the-art language models.
Their impact on a wide range of NLP tasks such as translation, summarization, Q&A, and more.
References & Further Reading
“Attention Is All You Need” (Vaswani et al., 2017)
https://arxiv.org/abs/1706.03762
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://arxiv.org/abs/1810.04805
GPT Series (OpenAI)
https://openai.com/research/