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This book serves as a concise, illustrated guide designed to deepen the reader’s understanding of large language models, making it an invaluable resource for interview preparation, project work, or simply satisfying intellectual curiosity. The guide is organized into five comprehensive sections:
– Foundations: This section provides a primer on neural networks and introduces key deep learning concepts essential for training and evaluation.
– Embeddings: Here, you will get to explore tokenization algorithms, delve into word-embeddings (word2vec), and understand sentence embeddings (RNN, LSTM, GRU).
– Transformers: Discover the motivation behind the self-attention mechanism of Transformers. Gain a detailed insight into the encoder-decoder architecture and its variations, including BERT, GPT, and T5. Additionally, learn useful tips and tricks to accelerate computations.
– Large language models: This section presents main techniques to fine-tune Transformer-based models, such as prompt engineering, parameter-efficient finetuning, and preference tuning.
– Applications: Understand the solutions to the most common challenges, including sentiment extraction, machine translation, retrieval-augmented generation, and much more.
Immerse yourself in this comprehensive guide and unlock the potential of large language models in your work and research.