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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 Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

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Frequently bought together

Modern Generative AI with ChatGPT and OpenAI Models

€37.99

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

€44.99

50 Algorithms Every Programmer Should Know

€37.99

Total € 120.97

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Sandra Patricia Bermudez Kasperczak Feb 22, 2024

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Timothy J. Spann Nov 09, 2023

This is a great reference book to check whenever you are going to be using common algorithms including some directly linked to machine learning. Following through this entire book in order would be a great way to get started in Com Sci, Python and/or Data Science.This is a good book to have in your library if you are a Data Engineer, Data Scientist or Python Programmer.

Amazon Verified review

William Appelbe Oct 11, 2023

This book is an invaluable encyclopedia of useful algorithms. How to apply them, pitfalls and best practices. I really like the focus of modern Machine Learning (ML) and AI, including large language models (used by ChatGPT). Far too often, as a contractor and trainer, I see people who do not understand algorithms and take a package (with its builtin algorithms) and blindly use it and get bad results. This is especially true in ML and AII really like the fact that the book uses Python and Google collab and all code is available for download. With collab, too, you don’t need to install anything (collab is a free Python/Jupyter notebook hosted in the Cloud) to run the examples.One warning is that this book is not a “quick read”, nor will it make you an instant algorithm guru. It assumes intermediate programming skill and intermediate knowledge of math (high school Y12). That is because the author deliberately explains how the algorithms work and why and how to tune, not just which algorithm to use.The good news is that you do not have to read the book “cover to cover” to use it. Each section of the book is largely self-contained and well-organized (like an encyclopedia). So if you are interested in Ml Language models or recommender engines you can just read those chapters/sections; and ship the first section on Sorting algorithms (although familarity with them is a skill every programmer should have, even if in most cases the sorting algorithms are already implemented in most frameworks).

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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