Free Download Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms

Free Download Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms

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Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms

Free Download Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms

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Book Description

How to Simulate the Mind

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About the Author

Nikhil Buduma is a computer science student at MIT with deep interests in machine learning and the biomedical sciences. He is a two time gold medalist at the International Biology Olympiad, a student researcher, and a â??hacker.â? He was selected as a finalist in the 2012 International BioGENEius Challenge for his research on the pertussis vaccine, and served as the lab manager of the Veregge Lab at San Jose State University at the age of 16. At age 19, he had a first author publication on using protist models for high throughput drug screening using flow cytometry. Nikhil also has a passion for education, regularly writing technical posts on his blog, teaching machine learning tutorials at hackathons, and recently, received the Young Innovator Award from the Gordon and Betty Moore Foundation for re-invisioning the traditional chemistry set using augmented reality.

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Product details

Paperback: 298 pages

Publisher: O'Reilly Media; 1 edition (June 29, 2017)

Language: English

ISBN-10: 9781491925614

ISBN-13: 978-1491925614

ASIN: 1491925612

Product Dimensions:

7 x 0.5 x 9.2 inches

Shipping Weight: 1.2 pounds (View shipping rates and policies)

Average Customer Review:

3.6 out of 5 stars

31 customer reviews

Amazon Best Sellers Rank:

#235,535 in Books (See Top 100 in Books)

Assumes reader has knowledge of algebra and calculus. Each chapter is written as the author is in a hurry just to cover the subject but leaves the reader with more questions.Learn more from luis serrano intro de deep learning on youtube, he explained better how perceptrons together to neural networks can model non linear systems.Tensorflow is outdated, it is much easier to follow the tutorial given by google with keras.Wasted $30.

The book assumes that the reader has some understanding of information theory, differential calculus and linear programming. The content would be ideal for university students whose memory or grasp of these topics would be fresh, like from last semester or year. The same unfortunately cannot be said of many software engineers who have been out of touch with these concepts for a long time. So pick this book based on your background.

It is a very good simple intuitive non-mathematical introduction to Neural Network for image recognition.

The theory of the book is solid, but none of the code works. This makes it difficult to follow the concepts

Assumes the reader knows linear algebra, calculus, and statistics. It can also a little difficult to follow/boring, ot would be better if the book was longer and more comprehensive

The Fundamentals of Deep Learning is a fantastic guide to deep learning for anyone looking for a solid understanding of an emerging field moving at a breakneck pace. It begins with a clear introduction to the primary building blocks (perceptron, basic calculus, gradient descent, etc.) and ends with cutting-edge techniques in visual recognition, natural language processing, and artificial intelligence (reinforcement learning). It conveys these complex and incredibly useful ideas through a steady progression of topics that is surprisingly digestible -- along with exercises for the reader to actually implement what they learn. I'd highly recommend this book for anyone looking for a comprehensive yet concise overview of modern deep learning.

Constantly assuming you have a solid background on almost all the concepts the author is trying to teach.. not a great hands-on book. Buy "Make Your Own Neural Network" for the first couple chapters, 1000% better explanation on both implementing neural nets and the math behind it. Disappointed.

Well written book giving an accessible, yet thorough explanation of the main topics of deep learning. I liked the effort the authors put into walking through mathematical derivations, application examples and visualizations. For the programmers, the book also include code samples so you understand everything from the theoretical principles to the practical implementation of CNNs, LSTMs and more !

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Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms PDF

Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms PDF

Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms PDF

Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms PDF