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

Understanding the Seq2Seq model

Following our exploration of autoencoders, another groundbreaking architecture in the realm of advanced sequential models is the Seq2Seq model. Central to many state-of-the-art natural language processing tasks, the Seq2Seq model exhibits a unique capability: transforming an input sequence into an output sequence that may differ in length. This flexibility allows it to excel in challenges like machine translation, where the source and target sentences can naturally differ in size.

Refer to Figure 11.3, which visualizes the core components of a Seq2Seq model:

Figure 11.3: Illustration of the Seq2Seq model architecture

Broadly, there are three main elements:

  • Encoder: Processes the input sequence
  • Thought vector: A bridge between the encoder and decoder
  • Decoder: Generates the output sequence

Let us explore them one by one.

Encoder

The encoder is shown as Figure 11.3. As we can observe, it is an input...