Ebook Download Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence (Undergraduate Topics in Computer Science)
Ebook Download Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence (Undergraduate Topics in Computer Science)
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Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence (Undergraduate Topics in Computer Science)
Ebook Download Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence (Undergraduate Topics in Computer Science)
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From the Back Cover
This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website. Topics and features: Introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learningDiscusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural networkExamines convolutional neural networks, and the recurrent connections to a feed-forward neural networkDescribes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learningPresents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology. Dr. Sandro Skansi is an Assistant Professor of Logic at the University of Zagreb and Lecturer in Data Science at University College Algebra, Zagreb, Croatia.
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About the Author
Dr. Sandro Skansi is an Assistant Professor of Logic at the University of Zagreb and Lecturer in Data Science at University College Algebra, Zagreb, Croatia.
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Product details
Series: Undergraduate Topics in Computer Science
Paperback: 191 pages
Publisher: Springer; 1st ed. 2018 edition (February 6, 2018)
Language: English
ISBN-10: 9783319730035
ISBN-13: 978-3319730035
ASIN: 3319730037
Product Dimensions:
6.1 x 0.5 x 9.2 inches
Shipping Weight: 10.9 ounces (View shipping rates and policies)
Average Customer Review:
3.8 out of 5 stars
4 customer reviews
Amazon Best Sellers Rank:
#425,744 in Books (See Top 100 in Books)
I am not sure how a book with this very bad quality made it to a publisher such as Springer. There are some deep learning books out there that are written by non-experts that are much better than this one. The book does not touch on any subject in any real substantial way. There are no examples of deep learning applications. The notation is vague. The Python code is presented inside the book which is rather hard to read there. The figures are really really of bad quality. There are no insights on the concepts at all. The research questions at the end of the book are meaningless and some them seemed as if they were written by someone who does not know what he is talking about. The book is a total waste of money.
Definitively recommended this book if have an interest in:1) A historical perspective of how machine learning evolved into deep learning during the past 50 years2) A self-contained and succint description of what are the deep learning mathematical pre-requisites (such as calculus, matrix computation, probabilities)3) A well structured introduction to:- Machine Learning basics- Convolutional network. This exposition is very well done.- Recurrent Networks. Another well-done exposition.- Autoencoders.I've also appreciated particularly the short overview of deep learning for NPL. Short, but very clear.One thing that is missing in this book is the use of Deep Learning together with Reinforcement Learning.So for that you need another source.
I developed a course in Deep Learning at the University of Washington. Although I assigned readings from the Goodfellow et al. book, I also recommended sections from this book. In particular, a two items stood out:1) A nice historical perspective on the subject, which is lacking in many students (and even young researchers!) today.2) Detailed numeric examples of backpropagation through a multi-layer network. I required my students to do the computations themselves with a calculator on a very small example, instead of just relying on the "magic" of auto-differentiation software such as that included in TensorFlow or pytorch. This book also gives a straightforward derivation of the backpropagation formulas.Although the book is short, it covers the necessary basics and then moves on to include not only standard feedforward deep networks, but also the basic convolutional neural nets (CNNs), recurrent neural nets (RNNs), autoencoders, and language models (word2vec), among others. Practical issues such as regularization (L1, L2, dropout) and momentum are discussed. This gives the reader a firm foundation for understanding more sophisticated recent (as of early 2019) models such as the Transformer, BERT, or GPT-2.The mathematical notation in this book is much easier to follow than in more advanced texts, and I think it's a perfect place to start.
The book has been succinctly written, where the author touches on an appropriate amount of introductory content and the history behind neural networks. He proceeds to then take the reader through the fundamentals required for allowing them to continue but provides an emphasis from a logical viewpoint.The remainder of the book intuitively covers off all concepts, approaches and variants of algorithms by using visual illustration, coupled with mathematical formulae and python code.The book can serve as a reference guide, as well as a good basis for anybody wishing to get into the space of statistical and machine learning.The python illustrations could have been formatted a little better by the publisher, however, I will not downvote as the content is what draws more importance to the subject.On a separate note, I'd like to add that the 1-star rating from another gentleman is completely and utterly unrealistic, so I will not comment on what possessed them to such a rating.
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