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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 architecture of different neural networks

Neural networks come in various types, each with a specific architecture suited to a different kind of task. The following list contains general descriptions of some of the most common types:

  • Feedforward neural network (FNN): This is the most straightforward type of neural network. Information in this network moves in one direction only, from the input layer through any hidden layers to the output layer. There are no cycles or loops in the network; it’s a straight, “feedforward” path.
Figure 6.2 – Feedforward neural network

Figure 6.2 – Feedforward neural network

  • Multilayer perceptron (MLP): An MLP is a type of feedforward network that has at least one hidden layer in addition to its input and output layers. The layers are fully connected, meaning each neuron in a layer connects with every neuron in the next layer. MLPs can model complex patterns and are widely used for tasks such as image recognition...

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