Complete Guide to Transformers: RNNs, Attention & BERT Explained

Embark on a comprehensive journey into the world of Natural Language Processing (NLP), culminating in a deep understanding of the revolutionary Transformer architecture. This full course meticulously builds your knowledge from the ground up. We begin with an introduction to NLP and its significance, then dive into the fundamentals of Recurrent Neural Networks (RNNs). You'll learn how RNNs function, their structure, the concept of hidden states, and how weights and biases operate across time steps, complete with mathematical formulations and an exploration of backpropagation. We'll cover various RNN architectures (Many-to-One, Many-to-Many, One-to-Many, One-to-One) and guide you through building your first RNN model in PyTorch, including text pre-processing, vocabulary building, zero padding, and data preparation. Next, we address RNN limitations and introduce crucial concepts like Word Embeddings, exploring popular methods such as Skip-gram, GloVe, and FastText, along with their practical implementation. The course then transitions to more advanced RNN structures, focusing on the powerful Encoder-Decoder architecture, discussing teacher forcing, and demonstrating its implementation. The limitations of traditional sequence-to-sequence models pave the way for the game-changing Attention Mechanism. You'll gain a thorough understanding of how attention works and the specifics of Self-Attention calculations. This knowledge forms the bedrock for understanding the Transformer architecture itself. Finally, we explore state-of-the-art Transformer models like BERT, delving into its pre-training and fine-tuning processes. We'll also touch upon the T5 model, compare BERT and GPT, and even demonstrate using a pre-trained model for a practical task like generating headlines. This course provides both the theoretical underpinnings and practical insights needed to master modern NLP techniques. Chapters- 0:00 - Introduction 1:12 = Recurrent Neural Networks 2:19 - How RNN functi