Book Image

50 Algorithms Every Programmer Should Know - Second Edition

By : Imran Ahmad
4 (5)
Book Image

50 Algorithms Every Programmer Should Know - Second Edition

4 (5)
By: Imran Ahmad

Overview of this book

The ability to use algorithms to solve real-world problems is a must-have skill for any developer or programmer. This book will help you not only to develop the skills to select and use an algorithm to tackle problems in the real world but also to understand how it works. You'll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, with the help of practical examples. As you advance, you'll learn about linear programming, page ranking, and graphs, and will then work with machine learning algorithms to understand the math and logic behind them. Case studies will show you how to apply these algorithms optimally before you focus on deep learning algorithms and learn about different types of deep learning models along with their practical use. You will also learn about modern sequential models and their variants, algorithms, methodologies, and architectures that are used to implement Large Language Models (LLMs) such as ChatGPT. Finally, you'll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks. By the end of this programming book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.
Table of Contents (22 chapters)
Free Chapter
1
Section 1: Fundamentals and Core Algorithms
7
Section 2: Machine Learning Algorithms
14
Section 3: Advanced Topics
20
Other Books You May Enjoy
21
Index

Delving into self-attention

Let’s consider again the ancient art of hieroglyphs, where symbols were chosen intentionally to convey complex messages. Self-attention operates in a similar manner, determining which parts of a sequence are vital and should be emphasized.

Illustrated in Figure 11.6 is the beauty of integrating self-attention within sequential models. Think of the bottom layer, churning with bidirectional RNNs, as the foundational stones of a pyramid. They generate what we call the context vector (c2), a summary, much like a hieroglyph would for an event.

Each step or word in a sequence has its weightage, symbolized as α. These weights interact with the context vector, emphasizing the importance of certain elements over others.

Imagine a scenario wherein the input Xk represents a distinct sentence, denoted as k, which spans a length of L1. This can be mathematically articulated as:

Here, every element, , represents a word or token from...