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    50 Algorithms Every Programmer Should Know: An unbeatable arsenal of algorithmic solutions for real-world problems, 2nd edition

    Posted By: yoyoloit
    50 Algorithms Every Programmer Should Know: An unbeatable arsenal of algorithmic solutions for real-world problems, 2nd edition

    50 Algorithms Every Programmer Should Know
    by Imran Ahmad, Ph.D

    English | 2023 | ISBN: 1803247762 | 539 pages | True/Retail PDF EPUB | 31.61 MB




    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 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.
    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

    Who this book is 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.
    Table of Contents

    Core Algorithms
    Data Structures
    Sorting and Searching Algorithms
    Designing Algorithms
    Graph Algorithms
    Unsupervised Machine Learning Algorithms
    Supervised Learning Algorithms
    Neural Network Algorithms
    Natural Language Processing
    Sequential Models
    Advanced Machine Learning Models
    Recommendation Engines
    Algorithmic Strategies for Data Handling
    Large-Scale Algorithms
    Evaluating Algorithmic Solutions
    Practical Considerations



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