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50 Algorithms Every Programmer Should Know

50 Algorithms Every Programmer Should Know

By : Imran Ahmad
4.5 (68)
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50 Algorithms Every Programmer Should Know

50 Algorithms Every Programmer Should Know

4.5 (68)
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)
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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

Using large-scale algorithms in cloud computing

The rapid growth of data and the increasing complexity of machine learning models have made distributed model training an essential component of modern deep learning pipelines. Large-scale algorithms demand vast amounts of computational resources and necessitate efficient parallelism to optimize their training times. Cloud computing offers an array of services and tools that facilitate distributed model training, allowing you to harness the full potential of resource-hungry, large-scale algorithms.

Some of the key advantages of using the Cloud for distributed model training include:

  • Scalability: The Cloud provides virtually unlimited resources, allowing you to scale your model training workloads to meet the demands of large-scale algorithms.
  • Flexibility: The Cloud supports a wide range of machine learning frameworks and libraries, enabling you to choose the most suitable tools for your specific needs.
  • Cost...
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