Best Reference Books – Fundamentals of Algorithms

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We have compiled the list of Top 10 Best Reference Books on Fundamentals of Algorithms subject. These books are used by students of top universities, institutes and colleges. Here is the full list of top 10 best books on Fundamentals of Algorithms along with reviews.

Kindly note that we have put a lot of effort into researching the best books on Fundamentals of Algorithms subject and came out with a recommended list of top 10 best books. The table below contains the Name of these best books, their authors, publishers and an unbiased review of books on "Fundamentals of Algorithms" as well as links to the Amazon website to directly purchase these books. As an Amazon Associate, we earn from qualifying purchases, but this does not impact our reviews, comparisons, and listing of these top books; the table serves as a ready reckoner list of these best books.

1. “Fundamentals of Computer Algorithms(second edition)” by Sahni Horowitz

“Fundamentals of Computer Algorithms(second edition)” Book Review: This book acts as a textbook for upper and graduate level courses in algorithms. It covers a wide range of fundamental topics like Divide and conquer, the greedy method, dynamic programming, backtracking as well as branch and bound method. Adequate examples are presented in this book which help students with the correct implementation of the design techniques. Further the book emphasises more on design techniques than individual algorithms.

2. “Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms” by Nikhil Buduma and Nicholas Locascio

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“Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms” Book Review: This book emphasises mainly on fundamentals of deep learning including foundations of machine learning and neural networks and much more. It includes the process of training feed-forward neural networks, builds neural networks and also explains basics of reinforcement learning.

3. “Fundamentals of Neural Networks: Architectures, Algorithms and Applications” by FAUSETT

“Fundamentals of Neural Networks: Architectures, Algorithms and Applications” Book Review: This book includes information about introduction to neural networks which is written in an easy to read language. The book covers systematic discussions of major neural networks and also presents all the fundamentals relating to it. Examples and illustrations present in the book helps the reader understand concepts better.

4. “Fundamental of Computer Algorithms” by Ellis Horowitz

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“Fundamental of Computer Algorithms” Book Review: This book acts as a textbook for upper and graduate level courses in algorithms. It covers the fundamentals of design techniques and computer algorithms. Illustrative examples present in the book helps the reader in understanding the concepts well.

5. “Fundamentals of Machine Learning for Predictive Data Analytics – Algorithms, Worked Examples, and Case Studies” by John D Kelleher and Brian Mac Namee

“Fundamentals of Machine Learning for Predictive Data Analytics – Algorithms, Worked Examples, and Case Studies” Book Review: This book describes machine learning which is used in predictive data analysis. It includes detailed information about important machine learning approaches that are used in predictive data analytics, covering both theoretical concepts and practical applications. Worked examples illustrate and help the readers in different case studies. The book also presents techniques for evaluating prediction models and offers case studies which help in describing specific data analytics projects through each phase of development. It is mainly designed for undergraduate students in computer science, engineering, mathematics, or statistics. The book can also be helpful for graduate students in the same stream.

6. “Algorithms in C++: Fundamentals, Data Structures, Sorting, Searching” by Sedgewick

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“Algorithms in C++: Fundamentals, Data Structures, Sorting, Searching” Book Review: This book describes the recent comprehensive coverage of important algorithms and data structures. It is written by Christopher Van Wyk and Robert Sedgewick. The book includes recent C++ implementations which expresses different methods in a concise and direct way. In addition, this book helps programmers and provides them practical examples to test them on real applications.

7. “DATA MINING AND ANALYSIS:FUNDAMENTAL CONCEPTS AND ALGORITHMS” by MOHAMMED J ZAKI/ WAGNER MEIRA Jr

“DATA MINING AND ANALYSIS:FUNDAMENTAL CONCEPTS AND ALGORITHMS” Book Review: This book presents algorithms related to data mining and analysis from the basics. It describes methods for analyzing patterns and models of all kinds of data, with applications ranging from scientific discovery to business intelligence and analytics. The book serves as a textbook for undergraduate and graduate students in the field of data mining. Further the book discusses exploratory data analysis, pattern mining, clustering and its classification. In addition, it contains abundant examples with algorithmic perspective.

8. “Neural Network Fundamentals with Graphs, Algorithms and Applications” by N K Bose and Ping Liang

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“Neural Network Fundamentals with Graphs, Algorithms and Applications” Book Review: This book describes neural network theory for many diverse applications. It introduces the structure of artificial neural networks characterized by distinguishing classes of graphs. The book includes precise analysis of perceptron and lms-theory based neural networks. Further it covers multilayer feedforward networks and self-organizing competitive learning neural networks.

9. “Discrete Inverse Problems: Insight and Algorithms (Fundamentals of Algorithms)” by Per Christian Hansen

“Discrete Inverse Problems: Insight and Algorithms (Fundamentals of Algorithms)” Book Review: This book discusses practical treatment of inverse problems by means of numerical methods and focuses on basic mathematical as well as computational aspects. It includes tutorial exercises which help the reader in providing hands-on experience with different methods. This book is mainly designed for graduate students, researchers and professionals in engineering. It also helps readers with proper background knowledge to solve simple inverse problems.

10. “Machine Learning: Fundamental Algorithms for Supervised and Unsupervised Learning With Real-World Applications” by Joshua Chapmann

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“Machine Learning: Fundamental Algorithms for Supervised and Unsupervised Learning With Real-World Applications” Book Review: This book presents machine language in a clean and easy to understand language. It covers the fundamentals and helps readers with real world problems. The book also serves well for a starter who has no prior knowledge about machine learning. It can be helpful for undergraduate as well as graduate students and for those researching about neural networks and natural language processing.

People who are searching for Free downloads of books and free pdf copies of these top 10 books on Fundamentals of Algorithms – we would like to mention that we don’t have free downloadable pdf copies of these good books and one should look for free pdf copies from these Authors only if they have explicitly made it free to download and read them.

We have created a collection of best reference books on "Fundamentals of Algorithms" so that one can readily see the list of top books on "Fundamentals of Algorithms" and buy the books either online or offline.

If any more book needs to be added to the list of best books on Fundamentals of Algorithms subject, please let us know.

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Manish Bhojasia - Founder & CTO at Sanfoundry
Manish Bhojasia, a technology veteran with 20+ years @ Cisco & Wipro, is Founder and CTO at Sanfoundry. He lives in Bangalore, and focuses on development of Linux Kernel, SAN Technologies, Advanced C, Data Structures & Alogrithms. Stay connected with him at LinkedIn.

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