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This article showcases our top picks for the Best Books On Computer Science Theory. We reached out to industry leaders and experts who have contributed the suggestions within this article (they have been credited for their contributions below).
We are keen to hear your feedback on all of our content and our comment section is a moderated space to express your thoughts and feelings related (or not) to this article This list is in no particular order.
This practical book shows you how to use concrete examples, minimal theory. And two production-ready Python frameworks-Scikit-Learn and Tensor Flow-author Aurélien Géron, helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn various techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started exploring the machine learning landscape, particularly neural netsUse Scikit-Learn to track an example machine-learning project end-to-and explore several training models, including support vector machines. Decision trees. Random forests. And ensemble methods use the Tensor Flow library to build and train neural native into neural net architectures. Including convolutional nets, recurrent nets, and deep reinforcement. Learning. Learn techniques for training and scaling deep neural nets.
Programming Legend Charles Petzold unlocks the secrets of the extraordinary and prescient 1936 paper by Alan M. Turing. Mathematician Alan Turing invented the imaginary computer known as the Turing Machine; in an age before computers, he explored the concept of what it meant to be computable, creating the field of computability theory in the process, a foundation of present-day computer programming. The book expands Turing’s original 36-page paper with additional background chapters and extensive annotations; the author elaborates on and clarifies many of Turing’s statements, making the original difficult-to-read document accessible to present-day programmers, computer science majors, math geeks, and others.
The Self-Taught Programmer, an all-in-one guide for all neophytes who are serious about becoming professional operators. Aside from coding, Althoff also trains other skills needed to land and operate a job in a high-profile tech company. This book is not just about learning to compute; although you will study to code. If you want to program professionally, it is not enough to learn to code; that is why, besides promoting you learn to program, I also cover the rest of the things you need to identify to program professionally that classes and books don’t teach you. “The Self-taught Programmer” is a roadmap, a guide to reach you from writing your first Python program, to moving your first professional interview.
One of the most sought-after textbooks in the field of AI. The book offers the most extensive, state-of-the-art introduction to the theory and practice of artificial intelligence for modern applications. If you are someone without prior knowledge of the field, this is the best book to commence. Further in your study of advanced books on the subject, you won’t be missing out on any critical basic information related to artificial intelligence.
This product was recommended by Kerry Lopez from Incrementors
Grokking Algorithms book helps take on this core computer science topic. In this, you’ll learn how to use common algorithms to the practical programming difficulties you face every day. You’ll begin with tasks like searching and sorting. As you develop your abilities, you’ll tackle more complicated problems like data artificial intelligence and compression. Each accurately presented example includes helpful diagrams and fully explained code samples in Python. By the end of this book, you will learn widely relevant algorithms as well as how and when to apply them.
It used to be one of my favorites when I was a computer science engineer. It is a safe ticket to the analysis of different concepts related to algorithms, data structures and functions that makes the foundation of computer science data. It includes the most important and popular algorithms, therefore if you are a genuine programmer, you cannot survive without them. This book provides a strong basis for existing non common algorithms and data structures. Everything comes with good pseudo code, clear steps and lots of analysis to compare with. The most astonishing thing about this book is that it provides me with worst case, average case and best case scenarios which allows my mind to think and understand on a deeper level. I take this book as a bible for algorithms, no matter how many times I read this every time I get to learn something new about my subject.