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

Data representation for sequential models

Timesteps add depth to the data, making it a 3D structure. In the context of sequential data, each “unit” or instance of this dimension is termed a “timestep.” This is crucial to remember: while the dimension is called “timesteps,” each individual data point in this dimension is a “timestep.” Figure 10.4 illustrates the three dimensions in data used for training RNNs, emphasizing the addition of timesteps:

Figure 10.4: The 3D data structures used in RNN training

Given that the concept of timesteps is a new addition to our exploration, a special notation is introduced to represent it effectively. A superscript enclosing a timestep in angle brackets is paired with the variable in question. For example, using this notation, and represent the value of the variable stock_price at timestep t1 and timestep t2, respectively.

The choice of dividing data into batches, essentially...