Reddit Reddit reviews Deep Learning (Adaptive Computation and Machine Learning series)

We found 18 Reddit comments about Deep Learning (Adaptive Computation and Machine Learning series). Here are the top ones, ranked by their Reddit score.

Computers & Technology
Books
Computer Science
AI & Machine Learning
Artificial Intelligence & Semantics
Deep Learning (Adaptive Computation and Machine Learning series)
The MIT Press
Check price on Amazon

18 Reddit comments about Deep Learning (Adaptive Computation and Machine Learning series):

u/ajh2148 · 11 pointsr/computerscience

I’d personally recommend Andrew Ng’s deeplearning.ai course if you’re just starting. This will give you practical and guided experience to tensorflow using jupyter notebooks.

If it’s books you really want I found the following of great use in my studies but they are quite theoretical and framework agnostic publications. Will help explain the theory though:

Deep Learning (Adaptive Computation and Machine Learning Series) https://www.amazon.co.uk/dp/0262035618/ref=cm_sw_r_cp_api_i_Hu41Db30AP4D7

Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series) https://www.amazon.co.uk/dp/0262039249/ref=cm_sw_r_cp_api_i_-y41DbTJEBAHX

Pattern Recognition and Machine Learning (Information Science and Statistics) (Information Science and Statistics) https://www.amazon.co.uk/dp/0387310738/ref=cm_sw_r_cp_api_i_dv41DbTXKKSV0

Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) https://www.amazon.co.uk/dp/B00AF1AYTQ/ref=cm_sw_r_cp_api_i_vx41DbHVQEAW1

u/otterom · 6 pointsr/ProgrammerHumor

I have it. Pretty heavy for my tiny brain.

But, anyway, Amazon has it for ~$56. In the world of expensive textbooks, this is a steal.


https://www.amazon.com/dp/0262035618/ref=cm_sw_r_cp_apa_i_G4DJDbH8JXJ71

u/weelod · 3 pointsr/artificial

piggybacking on what /u/T4IR-PR said, the best book to attack the science aspect of AI is Artifical Intelligence: A Modern Approach. It was the standard AI textbook when I took the class and it's honestly written very well - people with a basic undergraduate understanding of cs/math can jump right in and start playing with the ideas it presents, and it gives you a really nice outline of some of the big ideas in AI historically. It's one of the few CS textbooks that I recommend people buy the physical copy of.

Note that a lot of the field of AI has been moving more towards ML, so if you're really interested I would look into books regarding that. I don't know what intro texts you would want to use, but I personally have copies of the following texts that I would recommend

  • Machine Learning (Murphy)
  • Deep Learning Book (Goodfellow , Bengio)

    and to go w/ that

  • All of Statistics (Wasserman)
  • Information Theory (Mackay)

    for some more maths background, if you're a stats/info theory junky.

    After all that, if you're more interested in a philosophy/theoretical take on AI then I think Superintelligence is good (I've heard?)
u/daniel451 · 3 pointsr/de_IAmA

Zum "Rumspielen" für erste Erfahrungen bietet sich auch Keras sehr gut an, dass ist eine High-Level-API für Theano/TensorFlow, zu der es auch viele Git repos und tutorials gibt.

Darüberhinaus würde ich mittlerweile auch TensorFlow empfehlen. Ich bin von einem Einstieg mit C++/Caffe zu Python/Theano gekommen und nach kurzem Ausflug zu PyTorch seit mittlerweile fast 2 Jahren nur noch mit TensorFlow unterwegs. PyTorch gefiel mir persönlich einfach weniger als TensorFlow und Caffe/Theano usw. sind imho weniger mächtig als TensorFlow bei zeitgleich häufig größerer Komplexität.

So, genug der Google-Framework-Werbung ;)

Den Kurs von Udacity habe ich persönlich noch nicht belegt, kenne ihn aber und wurde mir bisher nur gutes berichtet. Ich persönlich finde Andrew Ng (Stanford) ist in der Lehre sehr fähig und nicht nur ein extremer Pionier auf dem Gebiet AI (arbeitet unter Anderem ja auch für Baidu, dem chinesischen Google). Seinen Kurs Deep Learning auf Coursera kann ich nur wärmstens empfehlen.

Abgesehen davon ist das Buch Deep Learning von Goodfellow & Bengio (beide ebenfalls Pioniere auf dem Gebiet AI) imho eines der besten Fachbücher in dem Bereich.

Ansonsten kannst du gerne weitere Fragen posten. Machine Learning und besonders neuronale Netze (deep learning) ist genau das, worauf ich mich seit Langem spezialisiere. Seit Längerem mache ich im Bereich AI übrigens kaum etwas anderes, weil dies alle anderen Ansätze verdrängt hat. Hinter so gut wie allem was man heutzutage von AI hört steckt machine learning und moderne, qualitative Ansätze (sehr populär etwa die Google Search, Google Translator) sind immer neuronale Netze, bzw. präziser deep learning.

u/ase1590 · 2 pointsr/learnprogramming

If your interested in deep learning, this new book will be out soon. No idea how comprehensive it is, as it's not released and I haven't gone through it myself, but it definitely won't be dated. Not really sure what topics and things you want to cover

Deep Learning (Adaptive Computation and Machine Learning series) https://www.amazon.com/dp/0262035618/ref=cm_sw_r_cp_apa_wzElybWRMMN8M

You can take a look at his examples and most of the book at his website

u/Calibandage · 2 pointsr/rstats

Deep Learning With Python is very good for practical application, as is the course at fast.ai. For theory, people love Goodfellow.

u/APC_ChemE · 1 pointr/EngineeringStudents

This is a great book that takes you from chapter 1 in linear algebra and goes into machine learning with neural networks.

​

https://www.amazon.com/gp/product/0262035618/ref=ppx_yo_dt_b_asin_title_o08_s00?ie=UTF8&psc=1

​

The authors also have a website with some of the material.

​

https://www.deeplearningbook.org/

u/Pallidium · 1 pointr/neuroscience

Applying convolution in artificial neural networks was actually inspired by a simple model of the visual cortex (i.e. in the brain). If you want to read a fully technical overview, I'd suggest the section "The Neuroscientific Basis for Convolutional Networks" in chapter 9 of this book.

I'm gonna try to keep this post short and do a quick summary right now. Essentially, at early stages of visual processing the difference in activity between adjacent photoreceptor cells in the eye is taken, mostly due to lateral inhibitory connections on both bipolar neurons and the downstream bipolar neurons. This is essentially a convolution operation - just as you may subtract the brightness of adjacent pixels from a central pixel in a 2D convolution, this is done in the retina using lateral inhibitory connections. The section in that deep learning textbook I posted implies that this occurs only in visual cortex, but it actually occurs in the retina and LGN as well. So just as in modern CNNs, there are stacks of convolution operations in the real brain.

Of course, the convolution that occurs in artificial neural networks is a simplification of the actual process that occurs in brains, but it was inspired by the functionality and organization of the brain.

u/TonySu · 1 pointr/learnprogramming

Probably start with Artificial Intelligence: a modern approach. This is the state of the art AI as of 2009, of course in AI years that's ancient history but it's background you must know if you're serious about AI.

Following on from that you have the very popular statistical techniques, you can read about these in Pattern Recognition and Machine Learning. These are a wide range of statistical models and algorithms that allow machines to infer, classify and predict. Another very important concept is Chapter 14 on combining models. IBM's Watson for example uses a complex network of "simple" models who combine their answers to form the final responses.

From all the techniques in the previous book, neural networks from Chapter 5 have become the most popular and powerful. These are covered in Deep Learning, and are currently the cutting edge of machine learning. They are extremely general models that seem to be highly successful at a range of tasks. In particular their popularity comes from their amazing accuracy in image recognition, which really challenged past algorithms.

Ultimately nothing you can learn from anyone is sure to bring you close to sci-fi AI. The techniques to produce such an AI eludes even the foremost experts. You may also become disillusioned with your dream as you realise just how mechanical and constrained AI is. I personally think we'd have better luck genetically engineering intelligence in a random animal/insect than creating true intelligence in silicon and circuits.

u/ziapelta · 1 pointr/learnmachinelearning

I really like Deep Learning by Ian Goodfellow, et al. You can but it from Amazon at https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618/ref=sr_1_1?ie=UTF8&qid=1472485235&sr=8-1&keywords=deep+learning+book. If you are a little cash strapped, there is an html version at http://www.deeplearningbook.org/. Of course, this book is specifically focused on neural networks as opposed to ML in general.

u/nickkon1 · 1 pointr/de

Ich arbeite gerade das Buch Deep Learning with Python durch und es ist schon mal besser als Onlinekurse, die ich in Deep Learning gemacht habe (Udemy Deep Learning A-Z). Es ist vom Entwickler von Keras (Python Tensorflow API) und er erklärt das Thema Neuronale Netze, geht etwas auf die Mathematik ein und widmet sich dann Keras bis hin zu somewhat State of the Art Lösungen. Das ist aber schon eine Unterkategorie von Data Science.

Sinnvoller ist am Anfang:

Das Buch bzw Amazon wird auch viel empfohlen und ist auf meiner nächsten Liste, kann aber nicht viel dazu sagen.

Ansonsten wird auch eigentlich überall der Coursera Machine Learning Kurs von Andrew Ng empfohlen. Auf Reddit/Github findet man dazu die entsprechenden Materialien in Python, wenn man kein MatLab machen will. Das ist für extrem viele der Einstiegskurs und sehr sinnvoll!

Kurse geben halt (meist für Geld) ein Zertifikat, was ein Vorteil ist. Bei Büchern hat man meist mehr Wissen und es ist intensiver als einfach ein paar Videos anzuschauen. Aber man hat leider nichts, was man wie ein Zertifikat vorweisen kann.

> Ist R zwingend notwendig?

Nein. Ich habe beides gelernt und würde sogar sagen, dass meist Python bevorzugt wird. Letztendlich ist es aber mMn egal. Oft lernen halt die, welche wie ich aus der Mathematik kommen, in der Uni schon R und benutzen es weiter. Oder andere, welche als Aktuar o.ä. im Finanzwesen gearbeitet haben und dort R benutzt haben, hören dann auch nicht plötzlich damit auf. Beides hat Vor-/Nachteile.

u/hurtja · 1 pointr/MachineLearning

I would start with reading.

For Neural Networks, I'd do:

  1. Deep Learning (Adaptive Computation and Machine Learning series) https://www.amazon.com/dp/0262035618/ref=cm_sw_r_cp_apa_i_nC11CbNXV2WRE

  2. Neural Networks and Learning Machines (3rd Edition) https://www.amazon.com/dp/0131471392/ref=cm_sw_r_cp_apa_i_OB11Cb24V2TBE

    For overview with NN, Fuzzy Logic Systems, and Evolutionary Algorithms, I recommend:

    Fundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems, and Evolutionary Computation (IEEE Press Series on Computational Intelligence) https://www.amazon.com/dp/1119214343/ref=cm_sw_r_cp_apa_i_zD11CbWRS95XY
u/pete0273 · 1 pointr/MachineLearning

It's only $72 on Amazon. It's mathematical, but without following the Theorem -> Proof style of math writing.

The first 1/3 of the book is a review of Linear Algebra, Probability, Numerical Computing, and Machine Learning.

The middle 1/3 of the book is tried-and-true neural nets (feedforward, convolutional, and recurrent). It also covers optimization and regularization.

The final 1/3 of the book is bleeding edge research (autoencoders, adversarial nets, Boltzmann machines, etc.).

The book does a great job of foreshadowing. In chapters 4-5 it frames problems with the algorithms being covered, and mentions how methods from the final 1/3 of the book are solving them.

https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618/

u/TheMiamiWhale · 1 pointr/MachineLearning

It really depends on your comfort and familiarity with the topics. If you've seen analysis before you can probably skip Rudin. If you've seen some functional analysis, you can skip the functional analysis book. Convex Optimization can be read in tandem with ESL, and is probably the most important of the three.

Per my other comment, if your goal is to really understand the material, it's important you understand all the math, at least in terms of reading. Unless you want to do research, you don't need to be able to reproduce all the proofs (to help you gauge your depth of understanding). In terms of bang for your buck, ESL and Convex Optimization are probably the two I'd focus on. Another great book Deep Learning this book is extremely approachable with a modest math background, IMO.