Imran Ahmad

Imran has been a part of cutting-edge research about Algorithms and Machine Learning for the last many years. He completed his PhD in 2010 in which he proposed a new Linear Programming based algorithm which can be used to optimally assign resources in a large scale cloud computing environment. In 2017, Imran developed a realtime analytics framework named StreamSensing. He has since authored multiple research papers that use StreamSensing to process multimedia data for various Machine Learning Algorithms. Imran is currently working at Advanced Analytics Solution Center (A2SC) at Canadian Federal Government as a Data Scientist where he is using Machine Learning Algorithms for critical use-cases. Imran is a visiting professor at Carleton University, Ottawa. Imran has also been teaching for Google and Learning Tree for the last many years. The topics Imran teaches include Algorithms, Cloud Computing and Deep Learning. Over his career, Imran has written many research papers and a couple of his recent papers have won the best paper award. Imran also regularly writes blogs on selected IT topics. In addition to his professional work, Imran is into Nature Photography. Over the years he has taken thousands of photos about nature. Imran's passion is to find a way to make technology work for the betterment of humanity. This passion is the main motivation behind his research.

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.