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Mastering NLP from Foundations to LLMs

Mastering NLP from Foundations to LLMs

By : Gazit, Meysam Ghaffari
4.9 (24)
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Mastering NLP from Foundations to LLMs

Mastering NLP from Foundations to LLMs

4.9 (24)
By: Gazit, Meysam Ghaffari

Overview of this book

Do you want to master Natural Language Processing (NLP) but don’t know where to begin? This book will give you the right head start. Written by leaders in machine learning and NLP, Mastering NLP from Foundations to LLMs provides an in-depth introduction to techniques. Starting with the mathematical foundations of machine learning (ML), you’ll gradually progress to advanced NLP applications such as large language models (LLMs) and AI applications. You’ll get to grips with linear algebra, optimization, probability, and statistics, which are essential for understanding and implementing machine learning and NLP algorithms. You’ll also explore general machine learning techniques and find out how they relate to NLP. Next, you’ll learn how to preprocess text data, explore methods for cleaning and preparing text for analysis, and understand how to do text classification. You’ll get all of this and more along with complete Python code samples. By the end of the book, the advanced topics of LLMs’ theory, design, and applications will be discussed along with the future trends in NLP, which will feature expert opinions. You’ll also get to strengthen your practical skills by working on sample real-world NLP business problems and solutions.
Table of Contents (14 chapters)
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The challenges of training language models

Training large language models is a complex and resource-intensive task that poses several challenges. Here are some of the key issues:

  • Computational resources: The training of large language models requires substantial computational resources. These models have billions of parameters that need to be updated during training, which involves performing a large amount of computation over an extensive dataset. This computation is usually carried out on high-performance GPUs or tensor processing units (TPUs), and the costs associated can be prohibitive.
  • Memory limitations: As the size of the model increases, the amount of memory required to store the model parameters, intermediate activations, and gradients during training also increases. This can lead to memory issues on even the most advanced hardware. Techniques such as model parallelism, gradient checkpointing, and offloading can be used to mitigate these issues, but they add complexity...

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