Deep Learning
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- Create Date：2021-09-20 05:51:09
- Update Date：2025-09-06
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- Author：John D. Kelleher
- ISBN：0262537559
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Summary
An accessible introduction to the artificial intelligence technology that enables computer vision， speech recognition， machine translation， and driverless cars。
Deep learning is an artificial intelligence technology that enables computer vision， speech recognition in mobile phones， machine translation， AI games， driverless cars， and other applications。 When we use consumer products from Google， Microsoft， Facebook， Apple， or Baidu， we are often interacting with a deep learning system。 In this volume in the MIT Press Essential Knowledge series， computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution。
Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets; its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power。 Kelleher also explains some of the basic concepts in deep learning， presents a history of advances in the field， and discusses the current state of the art。 He describes the most important deep learning architectures， including autoencoders， recurrent neural networks， and long short-term networks， as well as such recent developments as Generative Adversarial Networks and capsule networks。 He also provides a comprehensive （and comprehensible） introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation。 Finally， Kelleher considers the future of deep learning--major trends， possible developments， and significant challenges。
Deep learning is an artificial intelligence technology that enables computer vision， speech recognition in mobile phones， machine translation， AI games， driverless cars， and other applications。 When we use consumer products from Google， Microsoft， Facebook， Apple， or Baidu， we are often interacting with a deep learning system。 In this volume in the MIT Press Essential Knowledge series， computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution。
Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets; its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power。 Kelleher also explains some of the basic concepts in deep learning， presents a history of advances in the field， and discusses the current state of the art。 He describes the most important deep learning architectures， including autoencoders， recurrent neural networks， and long short-term networks， as well as such recent developments as Generative Adversarial Networks and capsule networks。 He also provides a comprehensive （and comprehensible） introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation。 Finally， Kelleher considers the future of deep learning--major trends， possible developments， and significant challenges。
Reviews
Nisham Mohammed
，
Does not work as an audiobook。 The general Info is present but the equations in the nitty gritty are completely lost。
Keith Swenson
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Nice short book on machine learning。 Very esoteric， but a good treatment。
Anthony O'Connor
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Excellent short introductionSeems to hit all the bases。 No fluff。 Moderately technical descriptions of the problems and algorithms。 Really quite informative。 Discusses recent work and possibilities。 The term ‘deep learning’ was a PR coup。 Multi layer neural networks does not read anywhere near so well。 But all that aside the truly astonishing thing is that whatever they are called they work so amazingly well。 Potentially changing everything。
Andrew
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A good introduction to Deep Learning。 While the book does contain quite a bit of mathematics it is not overly complex （the exception being the second to last chapter which for someone with no calculus experience became fairly dense）。 I wouldn't necessarily consider this a high-level overview， but I would consider it worth the time if you are interested in the subject of Deep Learning。 A good introduction to Deep Learning。 While the book does contain quite a bit of mathematics it is not overly complex （the exception being the second to last chapter which for someone with no calculus experience became fairly dense）。 I wouldn't necessarily consider this a high-level overview， but I would consider it worth the time if you are interested in the subject of Deep Learning。 。。。more
Sanjarbek
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As promised， a concise introduction to where the field of Deep Learning stands。 If you are already versed in DL and want a quick weekend read on an alternative view: 5 / 5。If you are about to start learning DL and want to dash through the field to draw a roadmap for yourself: 5 / 5。If you are looking for a reference book to study DL: 0 / 5。
Gurkiran
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This book gives beautiful insights on how exactly a Neural Network functions。 This helped me build a stronger intuition of the basics of Deep Learning。 It is quite a heavy read。 It even feels wordy at times。 However， it travels beautifully through the various fundamental concepts of deep learning。 It's as if I'm reading a story， and arriving at the various concepts， through some very clear explanations， and most importantly， building a strong intuition while doing so。 This book gives beautiful insights on how exactly a Neural Network functions。 This helped me build a stronger intuition of the basics of Deep Learning。 It is quite a heavy read。 It even feels wordy at times。 However， it travels beautifully through the various fundamental concepts of deep learning。 It's as if I'm reading a story， and arriving at the various concepts， through some very clear explanations， and most importantly， building a strong intuition while doing so。 。。。more
Aaron Acst
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The book is a good introduction to the basics of neural networks and the different algorithms used in the field， as a student ， the author does a great job explaining the perceptron， hidden layers， and different activation functions used， way much better than what I "learned" at school， after reading it I didn't have any problem following more complex books and videos about the topic， Excelent technical introduction and a brief history of deep learning。 Highly recomment it if you want to start l The book is a good introduction to the basics of neural networks and the different algorithms used in the field， as a student ， the author does a great job explaining the perceptron， hidden layers， and different activation functions used， way much better than what I "learned" at school， after reading it I didn't have any problem following more complex books and videos about the topic， Excelent technical introduction and a brief history of deep learning。 Highly recomment it if you want to start learning about this topic。 。。。more
Jerrid Kruse
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A great introduction that includes the mathematics as well as the conceptual foundations of deep learning。 The chapter outlining the history of AI was perhaps the most interesting and useful for seeing the development of the ideas。
Marco
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This book never really delivers on the promise of being "an accessible introduction" to deep learning， despite the fact that the two first chapters are written on a very clear language that strikes a good balance between precision and understandability for the layperson。 After that， however， the book does not find its voice: some parts seem to reach out to a general audience， but， only a few paragraphs later， the author goes back to speaking to an audience of computer scientists。 As a result， mu This book never really delivers on the promise of being "an accessible introduction" to deep learning， despite the fact that the two first chapters are written on a very clear language that strikes a good balance between precision and understandability for the layperson。 After that， however， the book does not find its voice: some parts seem to reach out to a general audience， but， only a few paragraphs later， the author goes back to speaking to an audience of computer scientists。 As a result， much of the core of the book is inaccessible to the general reader while failing to provide enough detail for a technically-minded introduction。 Which is sad， as the book occasionally delivers insights that are useful for either audience。 So， it seems that the author would have been better served by picking one public or the other， instead of aiming at both and satisfying neither。Besides this general complaint， there is one specific thing that annoyed me A LOT: figure positioning。 At times， a figure （such as a network diagram） is mentioned in the text， but it only appears several pages later， after the author has already spent a few paragraphs describing what is going on。 This does not help with understanding what is going on。 。。。more
Shawn
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A case of false advertising， but a highly informative read nonetheless。
Sam
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This is definitely very gentle， and very informative introduction to the deep learning。 To me it seems that this book even will be clear to people without prior experience with deep learning。 Kelleher dissects neural networks to their basic units and explains their basics along with mathematics （mostly high-level math， but for deeper understanding the reader can look for more technical books）。 Then， he puts all the elements together and after this there is no magic left under the hood of the dee This is definitely very gentle， and very informative introduction to the deep learning。 To me it seems that this book even will be clear to people without prior experience with deep learning。 Kelleher dissects neural networks to their basic units and explains their basics along with mathematics （mostly high-level math， but for deeper understanding the reader can look for more technical books）。 Then， he puts all the elements together and after this there is no magic left under the hood of the deep learning algorithms。 I think that this book will help everyone to get at least general understanding， what is deep learning， because it is not only modern buzzword， but the essential part of the modern informational systems。 。。。more
Brunston
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4-decent。 A readable introduction。 Useful to get an overhead/much-simplified view while learning about NNs and deep learning in depth in class。
sebastian odutola
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An excellent introduction to the theoretical basis for one of the most important technologies of the 21st century。 Thankfully， Kelleher doesn't completely eschew mathematics; even so at times more rigorous mathematical explanations may have aided understanding， at least for those mathematically inclined。 An excellent introduction to the theoretical basis for one of the most important technologies of the 21st century。 Thankfully， Kelleher doesn't completely eschew mathematics; even so at times more rigorous mathematical explanations may have aided understanding， at least for those mathematically inclined。 。。。more
Abdullah Shams
，
The book stays on the surface， and dives in momentarily into the depths。 Most of the content can and should be understood by non tech people who has entrust in Deep-learning or Machine Learning in general。 For me specifically， its provides an understanding of the general audience and how to approach them in terms of explaining and seeing the potential of Machine learning。
Kursad Albayraktaroglu
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A very clear， concise and highly readable introduction to deep learning。 It helped me strengthen my understanding of some key concepts in this area。
James Klagge
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Presents the what and how of AI in （pretty） clear and understandable ways。 Not too technical， though a few things were a bit iffy for me near the end of Ch 6。 But a good understanding of algebra and at least an acquaintance with calculus should suffice。 The book only touches on some of the ethical/social issues that can arise， and which are my interests， but that is not its purpose。 It serves its purpose well--to provide the "essential knowledge" behind AI。 Presents the what and how of AI in （pretty） clear and understandable ways。 Not too technical， though a few things were a bit iffy for me near the end of Ch 6。 But a good understanding of algebra and at least an acquaintance with calculus should suffice。 The book only touches on some of the ethical/social issues that can arise， and which are my interests， but that is not its purpose。 It serves its purpose well--to provide the "essential knowledge" behind AI。 。。。more
Matthew Roche
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It is very， very hard to write accessible popular science that attempts to explain systems that， while not particularly complex， depend on concepts that are not clear with middle-school math。This is a very good attempt at explaining the basic components of deep learning。 It doesn’t go to deep， nor gloss over too much。 The author does commendable service explaining the underlying math， and while not ‘breezy’ it is immensely useful。I found it simplifies topics that I struggled to grasp around back It is very， very hard to write accessible popular science that attempts to explain systems that， while not particularly complex， depend on concepts that are not clear with middle-school math。This is a very good attempt at explaining the basic components of deep learning。 It doesn’t go to deep， nor gloss over too much。 The author does commendable service explaining the underlying math， and while not ‘breezy’ it is immensely useful。I found it simplifies topics that I struggled to grasp around back propagation and how neurons optimized outcomes。 The chapter on back-propagation math was beyond my ability， but I could grasp the basic concepts。That is the purpose of this work。 It is not a how/to guide or a survey of technologies and platforms。 It will not meaningfully explain to you how speech recognition or self-driving works。 It will give you a working sense of what the pieces are and how the mechanism works。That is what I was looking for， and it delivered。 。。。more
Michael Corley
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This is an excellent text for explanation。 If you are good at math， you don't really need a more advanced text to provide an overview of the topic。 You would prefer something that is easily readable and provides an overview of the concepts at a level of detail necessary to actually perform the task。 This is an excellent text for explanation。 If you are good at math， you don't really need a more advanced text to provide an overview of the topic。 You would prefer something that is easily readable and provides an overview of the concepts at a level of detail necessary to actually perform the task。 。。。more
Brian Clegg
，
This is an entry in a series from the MIT Press that selects a small part of a topic （in this case， a subset of artificial intelligence） and gives it an 'essential knowledge' introduction。 The problem is， there seems to be no consistency over the target audience of the series。I previously reviewed Virtual Reality in the same series and it kept things relatively simple and approachable to the general reader， even if it did overdo the hype。 This book by John Kelleher starts gently， but by about ha This is an entry in a series from the MIT Press that selects a small part of a topic （in this case， a subset of artificial intelligence） and gives it an 'essential knowledge' introduction。 The problem is， there seems to be no consistency over the target audience of the series。I previously reviewed Virtual Reality in the same series and it kept things relatively simple and approachable to the general reader， even if it did overdo the hype。 This book by John Kelleher starts gently， but by about half way through it has become a full-blown simplified textbook with far too much in-depth technical content。 That's exactly what you don't want in a popular science title。What we get is plenty of detail of what deep learning-based systems are and how they work at the technical level， but there is practically nothing on how they fit with applications （unless you count playing games）， which are described but not really explained， nor is there anything much on the problems that arise when deep learning is used for real world applications。 There is a passing reference， admittedly to the difficulties of understanding how a deep learning AI system came to a decision and how this clashes with the EU's GDPR requirement for transparency and explanation， but if feels more like this is done to criticise the naivety of the legislation than the danger of using such systems。Similarly， I saw nothing about the dangers of deep learning systems using big data picking up on correlations that don't involve any causal link， nor does the book discuss the long tail problems that arise with inputs that are relatively uncommon and so are unlikely to turn up in the training data。 Similarly we read nothing about the dangers of adversarial attacks， which can fool the systems into misinterpreting inputs with tiny changes， or the difficulties such systems have with real， messy environments as opposed to the rigid rules of a game。Overall， the book is both pitched wrong and doesn't cover the aspects that really matter to the public。 It may well do fine as an introductory text for a computer science student， but that doesn't fit with the blurb on the back， which implies it is for public consumption。 。。。more
Thomas Dietert
，
This book satisfied most of the initial expectations I had about it's content: It provided a comprehensive overview of the origins of the field of machine learning and AI， covered all the technical bases of modern neural networks such as RNNs （for language processing）， CNNs （for image processing）， GANs （for game theory）， and concluded with a short but insightful comment on the "future of AI"。 The mathematical deep dives into foundational concepts such as neurons， weighted sums， activation functi This book satisfied most of the initial expectations I had about it's content: It provided a comprehensive overview of the origins of the field of machine learning and AI， covered all the technical bases of modern neural networks such as RNNs （for language processing）， CNNs （for image processing）， GANs （for game theory）， and concluded with a short but insightful comment on the "future of AI"。 The mathematical deep dives into foundational concepts such as neurons， weighted sums， activation functions， classic neural network models of just a few layers （that could learn boolean functions such as AND and XOR）， pre-training neural-nets with auto-encoders （to filter out extraneous features in data sets）， modern neural-net models like LSTM and the average CNN， the gradient-decent algorithm （for training "deep convolutional neural-networks" with dozens of layers）， and finally the nuances and differential-calculus behind the back-propagation algorithm （the solution to the "credit assignment" problem that afflicts gradient-decent in deep neural-nets）， we're all greatly appreciated and mostly-well explained。The reason I rated it 4/5 stars is because I felt that this book could have spent more time on the technical details。 I was reading the book for the in hopes of gaining broader social， technical， and mathematical contexts with respect to the subject of "deep-learning" in general-- and for the most part， I think I got that。 However， I did feel that the explanation of back-propagation deserved a bit more attention。 As someone who spends a fair amount of time more than the average person in familiarizing myself with mathematics relevant to engineering， I still felt that I had to re-read certain sentences and paragraphs， and stare at the equations and visual depictions more times than I expected， for the reason that the information wasn't presented as clearly as it could have been。 I felt the author elided a certain depth of explanation in order to assure the book had few enough pages to potentially appeal to more people than might actually finish it。 In conclusion， I enjoyed this book enough to readily recommend this book to anyone who wants a thorough introduction to the topic of "deep learning"。 Those with at least a light familiarity to differential calculus and a tangential understanding of Machine Learning would greatly benefit from reading; perhaps the average data-scientist， the software engineer working for an ML company， or the mathematician/computer scientist looking to expand the breadth of their mathematical world。 。。。more
Reference
google book https://www.google.com/search?tbm=bks&q=Deep Learning
hathitrust digital library https://babel.hathitrust.org/cgi/ls?q1=Deep Learning&field1=ocr&a=srchls&ft=ft&lmt=ft
The British Library https://bll01.primo.exlibrisgroup.com/discovery/search?query=any,contains,Deep Learning&tab=LibraryCatalog&search_scope=Not_BL_Suppress&vid=44BL_INST:BLL01&lang=en&offset=0