Solve classic computer science problems from fundamental algorithms, such as sorting and searching, to modern algorithms in machine learning and cryptography
Key Features:
Discussion on Advanced Deep Learning Architectures New chapters on sequential models explaining modern deep learning techniques, like LSTMs, GRUs, and RNNs and Large Language Models (LLMs) Explore newer topics, such as how to handle hidden bias in data and the explainability of the algorithms Get to grips with different programming algorithms and choose the right data structures for their optimal implementation
Book Description:
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 by understanding 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 even 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 will 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 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.
What You Will Learn:
Design algorithms for solving complex problems Become familiar with neural networks and deep learning techniques Explore existing data structures and algorithms found in Python libraries Implement graph algorithms for fraud detection using network analysis Work with machine learning algorithms to cluster similar tweets and process Twitter data in real time Create a recommendation engine that suggests relevant movies to subscribers Implement foolproof security using symmetric and as