Book Image

Mastering NLP from Foundations to LLMs

By : Lior Gazit, Meysam Ghaffari
Book Image

Mastering NLP from Foundations to LLMs

By: Lior 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)

Setting up an LLM application – API-based closed source models

When looking to employ models in general and LLMs in particular, there are various design choices and trade-offs. One key choice is whether to host a model locally in your local environment or to employ it remotely, accessing it via a communication channel. Local development environments would be wherever your code runs, whether that’s your personal computer, your on-premises server, your cloud environment, and so on. The choice you make will impact many aspects, such as cost, information security, maintenance needs, network overload, and inference speed.

In this section, we will introduce a quick and simple approach to employing an LLM remotely via an API. This approach is quick and simple as it rids us of the need to allocate unusual computation resources to host the LLM locally. An LLM typically requires amounts of memory and computation resources that aren’t common in personal environments.

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