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

The evolution of advanced sequential modeling techniques

In Chapter 10, Understanding Sequential Models, we touched upon the foundational aspects of sequential models. While they serve numerous use cases, they face challenges in grasping and producing the complex intricacies of human language.

We’ll begin our journey by discussing autoencoders. Introduced in the early 2010s, autoencoders provided a refreshing approach to data representation. They marked a significant evolution in natural language processing (NLP), transforming how we thought about data encoding and decoding. But the momentum in NLP didn’t stop there. By the mid-2010s, Seq2Seq models entered the scene, bringing forth innovative methodologies for tasks such as language translation. These models could adeptly transform one sequence form into another, heralding an era of advanced sequence processing.

However, with the rise in data complexity, the NLP community felt the need for more sophisticated...