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Key benefits

Familiarize yourself with advanced deep learning architectures

Explore newer topics, such as handling hidden bias in data and algorithm explainability

Get to grips with different programming algorithms and choose the right data structures for their optimal implementation

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 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.

Who is this book for?

This computer science book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code. Whether you are a beginner looking to learn the most used algorithms concisely or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful. Python programming experience is a must, knowledge of data science will be helpful but not necessary.

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

Delve into state-of-the-art algorithms for proficient Natural Language Processing illustrated with real-world examples

Create a recommendation engine that suggests relevant movies to subscribers

Grasp the concepts of sequential machine learning models and their foundational role in the development of cutting-edge LLMs

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

50 Algorithms Every Programmer Should Know

₹3723.99

C# 12 and .NET 8 – Modern Cross-Platform Development Fundamentals

₹4468.99

Modern Generative AI with ChatGPT and OpenAI Models

₹3723.99

Total ₹ 11,916.97

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Sandra Patricia Bermudez Kasperczak Feb 22, 2024

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Greg Wagner Nov 01, 2023

This book does a great job of covering the fundamental algorithm concepts and their related data structures in a easy to understand way with practical examples. The book covers topics from basic sorting and search algorithm to more advanced topics like cyrptography and machine learning. It's a good desk reference devs and grad students.

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Joe Chu Oct 10, 2023

This book contains strategies and methods that are used to design, create, and modify algorithms using Python. It further distinguishes itself from other books in its ability to explain data structures in Python and how they relate to algorithms. The book further breaks down the algorithm into its basic versions and applications. Examples would be Search Algorithms, PageRank Algorithms, and so on. Designing algorithms “from scratch” is not an easy task, moreover, this book explains it in detail step by step. Continuing, it goes through this process into Machine Learning, Neural Network, and Natural Language Processing Algorithms again using Python. I feel each of these sections could easily be a book on their own with their breadth of depth into each subject and breakdown. The book finishes up with some advanced topics such as Sequential Modeling and recommendation engines. It further delves into advanced concepts such as data handling and large-scale algorithms.

50 Algorithms Every Programmer Should Know: Tackle computer science challenges with classic to modern algorithms in machine learning, software design, data systems, and cryptography , Second Edition

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