I am surprised by how poorly written this book is. I eagerly bought it based on all the positive reviews it had received.
Bad mistake. Only a few of the reviews clearly state the obvious problems of this book. Oddly enough, these informative
reviews tend to attract aggressively negative comments of an almost personal nature.
The disconnect between the majority of cloyingly effusive reviews of this book and the reality of how it is written
is quite flabbergasting. I do not wish to speculate on the reason for this but it does sometimes does occur with
a first book in an important area or when dealing with pioneer authors with a cult following.
First of all, it is not clear who is the audience--the writing does not provide details at the level one
expects from a textbook. It also does not provide a good overview ("big picture thinking"). Advanced readers
would also not gain much because it is too superficial, when it comes to the advanced topics (final 35% of book).
More than half of this book reads like a bibliographic notes section of a book, and the authors seem
to be have no understanding of the didactic intention of a textbook (beyond a collation or importance sampling
of various topics). In other words, these portions read
like a prose description of a bibliography, with equations thrown in for annotation. The level of
detail is more similar to an expanded ACM Computing Surveys article rather than a textbook in
several chapters. At the other extreme of audience expectation, we have a review of linear algebra in the beginning,
which is a waste of useful space that could have been spent on actual explanations in other
chapters. If you don't know linear algebra already, you cannot really hope to follow
anything (especially in the way the book is written). In any case, the linear
algebra introduced in that chapter is too poorly written to even brush up on known material-- so who is that for?
As a practical matter, Part I of the book is mostly redundant/off-topic for a neural network book
(containing linear algebra, probability, and so on)
and Part III is written in a superficial way--so only a third of the book is remotely useful.
Other than a chapter on optimization algorithms (good description of algorithms like
Adam), I do not see even a single chapter that has done a half-decent job of presenting
algorithms with the proper conceptual framework. The presentation style is unnecessarily terse,
and dry, and is stylistically more similar to a research paper rather than a book.
It is understood that any machine learning book would have some mathematical sophistication, but the
main problem is caused by a lack of concern on part of the authors in promoting readability and an inability to
put themselves in reader shoes (surprisingly enough, some defensive responses to negative reviews tend to place
blame on math-phobic readers). At the end of the day, it is the author's responsibility to make
notational and organizational choices that are likely to maximize understanding.
Good mathematicians have excellent manners while choosing notation (you don't use nested
subscripts/superscripts/functions if you possess the clarity to do it more simply).
And no, math equations are not the same as algorithms-- only a small part of it. Where is the rest?
Where is the algorithm described? Where is the conceptual framework?
Where is the intuition? Where are the pseudocodes? Where are the illustrations? Where are the examples?
No, I am not asking for recipes or Python code. Just some decent writing, details, and explanations.
The sections on applications, LSTM and convolutional neural networks are hand-wavy at places and
read like "you can do this to achieve that." It is impossible to fully reconstruct the methods from the
description provided.
A large part of the book (including restricted Boltzmann machines)
is so tightly integrated with Probabilistic Graphical models (PGM), so that it loses its neural network focus.
This portion is also in the latter part of the book that is written in a rather superficial way and
therefore it implicitly creates another prerequisite of being very used to PGM (sort-of knowing it wouldn't be enough). .
Keep in mind that the PGM view of neural networks is not the dominant view today, from either a practitioner
or a research point of view. So why the focus on PGM, if they don't have the space to elaborate?
On the one hand, the authors make a futile attempt at promoting accessibility by discussing redundant
pre-requisites like basic linear algebra/probability basics. On the other hand, the PGM-heavy approach implicitly
increases the pre-requisites to include an even more advanced machine learning topic than neural networks
(with a 1200+ page book of its own). What the authors are doing is the equivalent of trying to teach someone
how to multiply two numbers as a special case of tensor multiplication. Even for RNNs with deterministic hidden states
they feel the need to couch it as a graphical model. It is useful to connect areas, but mixing them
is a bad idea. Look at Hinton's course. It does explain the connection between Boltzmann machines and PGM
very nicely, but one can easily follow RBM without having to bear the constant burden of a PGM-centric view.
One fact that I think played a role in these types of strategic errors of judgement is the fact that the
lead author is a fresh PhD graduate There is no substitute for experience when it comes to maturity
in writing ability (irrespective of how good a researcher someone is). Mature writers have the ability to put
themselves in reader shoes and have a good sense of what is conceptually important. The
authors clearly miss the forest from the trees, with chapter titles like "Confronting
the partition function." The book is an example of the fact that a first book in an important area with the name of
a pioneer author in it is not necessarily a qualification for being considered a good book.
I am not hesitant to call it out. The emperor has no clothes.
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Customer reviews
A surprisingly poor book--who is the audience?
Reviewed in the United States on September 27, 2017
I am surprised by how poorly written this book is. I eagerly bought it based on all the positive reviews it had received.Bad mistake. Only a few of the reviews clearly state the obvious problems of this book. Oddly enough, these informativereviews tend to attract aggressively negative comments of an almost personal nature.The disconnect between the majority of cloyingly effusive reviews of this book and the reality of how it is writtenis quite flabbergasting. I do not wish to speculate on the reason for this but it does sometimes does occur witha first book in an important area or when dealing with pioneer authors with a cult following.First of all, it is not clear who is the audience--the writing does not provide details at the level oneexpects from a textbook. It also does not provide a good overview ("big picture thinking"). Advanced readerswould also not gain much because it is too superficial, when it comes to the advanced topics (final 35% of book).More than half of this book reads like a bibliographic notes section of a book, and the authors seemto be have no understanding of the didactic intention of a textbook (beyond a collation or importance samplingof various topics). In other words, these portions readlike a prose description of a bibliography, with equations thrown in for annotation. The level ofdetail is more similar to an expanded ACM Computing Surveys article rather than a textbook inseveral chapters. At the other extreme of audience expectation, we have a review of linear algebra in the beginning,which is a waste of useful space that could have been spent on actual explanations in otherchapters. If you don't know linear algebra already, you cannot really hope to followanything (especially in the way the book is written). In any case, the linearalgebra introduced in that chapter is too poorly written to even brush up on known material-- so who is that for?As a practical matter, Part I of the book is mostly redundant/off-topic for a neural network book(containing linear algebra, probability, and so on)and Part III is written in a superficial way--so only a third of the book is remotely useful.Other than a chapter on optimization algorithms (good description of algorithms likeAdam), I do not see even a single chapter that has done a half-decent job of presentingalgorithms with the proper conceptual framework. The presentation style is unnecessarily terse, and dry, and is stylistically more similar to a research paper rather than a book. It is understood that any machine learning book would have some mathematical sophistication, but themain problem is caused by a lack of concern on part of the authors in promoting readability and an inability toput themselves in reader shoes (surprisingly enough, some defensive responses to negative reviews tend to placeblame on math-phobic readers). At the end of the day, it is the author's responsibility to makenotational and organizational choices that are likely to maximize understanding.Good mathematicians have excellent manners while choosing notation (you don't use nestedsubscripts/superscripts/functions if you possess the clarity to do it more simply).And no, math equations are not the same as algorithms-- only a small part of it. Where is the rest?Where is the algorithm described? Where is the conceptual framework?Where is the intuition? Where are the pseudocodes? Where are the illustrations? Where are the examples?No, I am not asking for recipes or Python code. Just some decent writing, details, and explanations.The sections on applications, LSTM and convolutional neural networks are hand-wavy at places and read like "you can do this to achieve that." It is impossible to fully reconstruct the methods from thedescription provided.A large part of the book (including restricted Boltzmann machines)is so tightly integrated with Probabilistic Graphical models (PGM), so that it loses its neural network focus.This portion is also in the latter part of the book that is written in a rather superficial way andtherefore it implicitly creates another prerequisite of being very used to PGM (sort-of knowing it wouldn't be enough). .Keep in mind that the PGM view of neural networks is not the dominant view today, from either a practitioneror a research point of view. So why the focus on PGM, if they don't have the space to elaborate?On the one hand, the authors make a futile attempt at promoting accessibility by discussing redundantpre-requisites like basic linear algebra/probability basics. On the other hand, the PGM-heavy approach implicitlyincreases the pre-requisites to include an even more advanced machine learning topic than neural networks(with a 1200+ page book of its own). What the authors are doing is the equivalent of trying to teach someonehow to multiply two numbers as a special case of tensor multiplication. Even for RNNs with deterministic hidden statesthey feel the need to couch it as a graphical model. It is useful to connect areas, but mixing themis a bad idea. Look at Hinton's course. It does explain the connection between Boltzmann machines and PGMvery nicely, but one can easily follow RBM without having to bear the constant burden of a PGM-centric view.One fact that I think played a role in these types of strategic errors of judgement is the fact that thelead author is a fresh PhD graduate There is no substitute for experience when it comes to maturityin writing ability (irrespective of how good a researcher someone is). Mature writers have the ability to putthemselves in reader shoes and have a good sense of what is conceptually important. Theauthors clearly miss the forest from the trees, with chapter titles like "Confrontingthe partition function." The book is an example of the fact that a first book in an important area with the name ofa pioneer author in it is not necessarily a qualification for being considered a good book.I am not hesitant to call it out. The emperor has no clothes.
Reviewed in the United States on September 27, 2017
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Top reviews from the United States
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Reviewed in the United States on September 27, 2017
Bad mistake. Only a few of the reviews clearly state the obvious problems of this book. Oddly enough, these informative
reviews tend to attract aggressively negative comments of an almost personal nature.
The disconnect between the majority of cloyingly effusive reviews of this book and the reality of how it is written
is quite flabbergasting. I do not wish to speculate on the reason for this but it does sometimes does occur with
a first book in an important area or when dealing with pioneer authors with a cult following.
First of all, it is not clear who is the audience--the writing does not provide details at the level one
expects from a textbook. It also does not provide a good overview ("big picture thinking"). Advanced readers
would also not gain much because it is too superficial, when it comes to the advanced topics (final 35% of book).
More than half of this book reads like a bibliographic notes section of a book, and the authors seem
to be have no understanding of the didactic intention of a textbook (beyond a collation or importance sampling
of various topics). In other words, these portions read
like a prose description of a bibliography, with equations thrown in for annotation. The level of
detail is more similar to an expanded ACM Computing Surveys article rather than a textbook in
several chapters. At the other extreme of audience expectation, we have a review of linear algebra in the beginning,
which is a waste of useful space that could have been spent on actual explanations in other
chapters. If you don't know linear algebra already, you cannot really hope to follow
anything (especially in the way the book is written). In any case, the linear
algebra introduced in that chapter is too poorly written to even brush up on known material-- so who is that for?
As a practical matter, Part I of the book is mostly redundant/off-topic for a neural network book
(containing linear algebra, probability, and so on)
and Part III is written in a superficial way--so only a third of the book is remotely useful.
Other than a chapter on optimization algorithms (good description of algorithms like
Adam), I do not see even a single chapter that has done a half-decent job of presenting
algorithms with the proper conceptual framework. The presentation style is unnecessarily terse,
and dry, and is stylistically more similar to a research paper rather than a book.
It is understood that any machine learning book would have some mathematical sophistication, but the
main problem is caused by a lack of concern on part of the authors in promoting readability and an inability to
put themselves in reader shoes (surprisingly enough, some defensive responses to negative reviews tend to place
blame on math-phobic readers). At the end of the day, it is the author's responsibility to make
notational and organizational choices that are likely to maximize understanding.
Good mathematicians have excellent manners while choosing notation (you don't use nested
subscripts/superscripts/functions if you possess the clarity to do it more simply).
And no, math equations are not the same as algorithms-- only a small part of it. Where is the rest?
Where is the algorithm described? Where is the conceptual framework?
Where is the intuition? Where are the pseudocodes? Where are the illustrations? Where are the examples?
No, I am not asking for recipes or Python code. Just some decent writing, details, and explanations.
The sections on applications, LSTM and convolutional neural networks are hand-wavy at places and
read like "you can do this to achieve that." It is impossible to fully reconstruct the methods from the
description provided.
A large part of the book (including restricted Boltzmann machines)
is so tightly integrated with Probabilistic Graphical models (PGM), so that it loses its neural network focus.
This portion is also in the latter part of the book that is written in a rather superficial way and
therefore it implicitly creates another prerequisite of being very used to PGM (sort-of knowing it wouldn't be enough). .
Keep in mind that the PGM view of neural networks is not the dominant view today, from either a practitioner
or a research point of view. So why the focus on PGM, if they don't have the space to elaborate?
On the one hand, the authors make a futile attempt at promoting accessibility by discussing redundant
pre-requisites like basic linear algebra/probability basics. On the other hand, the PGM-heavy approach implicitly
increases the pre-requisites to include an even more advanced machine learning topic than neural networks
(with a 1200+ page book of its own). What the authors are doing is the equivalent of trying to teach someone
how to multiply two numbers as a special case of tensor multiplication. Even for RNNs with deterministic hidden states
they feel the need to couch it as a graphical model. It is useful to connect areas, but mixing them
is a bad idea. Look at Hinton's course. It does explain the connection between Boltzmann machines and PGM
very nicely, but one can easily follow RBM without having to bear the constant burden of a PGM-centric view.
One fact that I think played a role in these types of strategic errors of judgement is the fact that the
lead author is a fresh PhD graduate There is no substitute for experience when it comes to maturity
in writing ability (irrespective of how good a researcher someone is). Mature writers have the ability to put
themselves in reader shoes and have a good sense of what is conceptually important. The
authors clearly miss the forest from the trees, with chapter titles like "Confronting
the partition function." The book is an example of the fact that a first book in an important area with the name of
a pioneer author in it is not necessarily a qualification for being considered a good book.
I am not hesitant to call it out. The emperor has no clothes.
A surprisingly poor book--who is the audience?
Reviewed in the United States on September 27, 2017
I am surprised by how poorly written this book is. I eagerly bought it based on all the positive reviews it had received.Reviewed in the United States on September 27, 2017
Bad mistake. Only a few of the reviews clearly state the obvious problems of this book. Oddly enough, these informative
reviews tend to attract aggressively negative comments of an almost personal nature.
The disconnect between the majority of cloyingly effusive reviews of this book and the reality of how it is written
is quite flabbergasting. I do not wish to speculate on the reason for this but it does sometimes does occur with
a first book in an important area or when dealing with pioneer authors with a cult following.
First of all, it is not clear who is the audience--the writing does not provide details at the level one
expects from a textbook. It also does not provide a good overview ("big picture thinking"). Advanced readers
would also not gain much because it is too superficial, when it comes to the advanced topics (final 35% of book).
More than half of this book reads like a bibliographic notes section of a book, and the authors seem
to be have no understanding of the didactic intention of a textbook (beyond a collation or importance sampling
of various topics). In other words, these portions read
like a prose description of a bibliography, with equations thrown in for annotation. The level of
detail is more similar to an expanded ACM Computing Surveys article rather than a textbook in
several chapters. At the other extreme of audience expectation, we have a review of linear algebra in the beginning,
which is a waste of useful space that could have been spent on actual explanations in other
chapters. If you don't know linear algebra already, you cannot really hope to follow
anything (especially in the way the book is written). In any case, the linear
algebra introduced in that chapter is too poorly written to even brush up on known material-- so who is that for?
As a practical matter, Part I of the book is mostly redundant/off-topic for a neural network book
(containing linear algebra, probability, and so on)
and Part III is written in a superficial way--so only a third of the book is remotely useful.
Other than a chapter on optimization algorithms (good description of algorithms like
Adam), I do not see even a single chapter that has done a half-decent job of presenting
algorithms with the proper conceptual framework. The presentation style is unnecessarily terse,
and dry, and is stylistically more similar to a research paper rather than a book.
It is understood that any machine learning book would have some mathematical sophistication, but the
main problem is caused by a lack of concern on part of the authors in promoting readability and an inability to
put themselves in reader shoes (surprisingly enough, some defensive responses to negative reviews tend to place
blame on math-phobic readers). At the end of the day, it is the author's responsibility to make
notational and organizational choices that are likely to maximize understanding.
Good mathematicians have excellent manners while choosing notation (you don't use nested
subscripts/superscripts/functions if you possess the clarity to do it more simply).
And no, math equations are not the same as algorithms-- only a small part of it. Where is the rest?
Where is the algorithm described? Where is the conceptual framework?
Where is the intuition? Where are the pseudocodes? Where are the illustrations? Where are the examples?
No, I am not asking for recipes or Python code. Just some decent writing, details, and explanations.
The sections on applications, LSTM and convolutional neural networks are hand-wavy at places and
read like "you can do this to achieve that." It is impossible to fully reconstruct the methods from the
description provided.
A large part of the book (including restricted Boltzmann machines)
is so tightly integrated with Probabilistic Graphical models (PGM), so that it loses its neural network focus.
This portion is also in the latter part of the book that is written in a rather superficial way and
therefore it implicitly creates another prerequisite of being very used to PGM (sort-of knowing it wouldn't be enough). .
Keep in mind that the PGM view of neural networks is not the dominant view today, from either a practitioner
or a research point of view. So why the focus on PGM, if they don't have the space to elaborate?
On the one hand, the authors make a futile attempt at promoting accessibility by discussing redundant
pre-requisites like basic linear algebra/probability basics. On the other hand, the PGM-heavy approach implicitly
increases the pre-requisites to include an even more advanced machine learning topic than neural networks
(with a 1200+ page book of its own). What the authors are doing is the equivalent of trying to teach someone
how to multiply two numbers as a special case of tensor multiplication. Even for RNNs with deterministic hidden states
they feel the need to couch it as a graphical model. It is useful to connect areas, but mixing them
is a bad idea. Look at Hinton's course. It does explain the connection between Boltzmann machines and PGM
very nicely, but one can easily follow RBM without having to bear the constant burden of a PGM-centric view.
One fact that I think played a role in these types of strategic errors of judgement is the fact that the
lead author is a fresh PhD graduate There is no substitute for experience when it comes to maturity
in writing ability (irrespective of how good a researcher someone is). Mature writers have the ability to put
themselves in reader shoes and have a good sense of what is conceptually important. The
authors clearly miss the forest from the trees, with chapter titles like "Confronting
the partition function." The book is an example of the fact that a first book in an important area with the name of
a pioneer author in it is not necessarily a qualification for being considered a good book.
I am not hesitant to call it out. The emperor has no clothes.
Images in this review
Reviewed in the United States on May 15, 2018
Reviewed in the United States on July 7, 2017
Top reviews from other countries
Simon
Wrong Pages!!
Reviewed in the United Kingdom on August 8, 2018
Simon
Reviewed in the United Kingdom on August 8, 2018
Images in this review
A. M. Douglas
Theoretical ML by leading practitioners.
Reviewed in the United Kingdom on July 18, 2020James
Great content for practitioners but poor physical quality
Reviewed in the United Kingdom on July 14, 2018Chinedu Pascal Ezenkwu
Great book for deep learning practioners who seek to go beyond applications
Reviewed in the United Kingdom on April 4, 2020Customers who bought this item also bought
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