Reddit reviews Deep Learning with Python

yes and no. For beginners, I recommend https://www.amazon.de/Deep-Learning-Python-Francois-Chollet/dp/1617294438 and https://www.reddit.com/r/learnmachinelearning/comments/bmj2zh/get_a_free_early_release_copy_of_handson_machine/

Not OP, but among those he listed, I think Chollet's book is the best combination of practical, code-based content and genuinely valuable insights from a practitioner. Its examples are all in the Keras framework, which Chollet developed as a high-level API to sit on top of a number of possible DL libraries. But with TensorFlow 2.0, the Keras API is now fundamental to how you would write code in this pretty dominant framework. It's also a very well-written book.

Ordinarily, I resist books that are too focused on one framework over another. I'd never personally want a DL book in Java, for instance. But I think Chollet's book is good enough to recommend regardless of the platform you intend to use, although it will certainly be more immediately useful if you are working with tf.Keras.

Fellow NLP'er here! Some of my favorites so far:

• Neural Network Methods in Natural Language Processing by Yoav Goldberg (2017). Okay for beginners, but even better as a reference work. I make sure to keep it close at hand.
  
• Introduction to Information Retrieval, aka the IR Book by Christopher Manning (2008).
  
• Deep Learning with Python by Francois Chollet. The first I book I recommend to anyone getting started with deep learning. I've read it cover to cover about three times.
  
• For programming in Python in general, I also really enjoyed Fluent Python by Luciano Ramalho (2015), which gave me my first glimpse of just how deep the Python rabbit hole goes.

Well if you're serious about taking a run at it, here are the resources I've used to teach myself. I'm self-educated, no degree myself and have been working with ML for the last year at one of those large tech companies I listed, so you can definitely do it. Build a small portfolio or contribute to open-source projects and start applying, it's that simple. Best of luck!

Deep Lizard playlists (beginner through reinforcement learning): https://www.youtube.com/channel/UC4UJ26WkceqONNF5S26OiVw/playlists

MIT Artificial Intelligence Podcast: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4

Don't know Python? Go here (they also have a Python2 class): https://www.codecademy.com/learn/learn-python-3

Deep Learning with Python (written by the guy who wrote the Keras API, which is the main model building API you should learn first, it works on top of tensorflow/theano/etc.): https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438/ref=sr_1_3?keywords=deep+learning+with+python&qid=1566500320&s=gateway&sr=8-3

Learn to use Google CoLab when possible, it gives you a free K80 GPU and when you exceed the limits of that you can use a local runtime and use your own hardware. This article has excellent tips and tricks for CoLab: https://medium.com/@oribarel/getting-the-most-out-of-your-google-colab-2b0585f82403

The Math: when if/you're wanting to dive into the nitty-gritty math details the "godfather of modern ML" as they say is Geoffrey Hinton who played a huge role in the invention of deep learning and the mathematical trick that makes it work, back-propagation. You'll need to brush up on differential calculus, summation notation and statistics, but this will help you build your own architectures should you want/need to: https://www.youtube.com/playlist?list=PLoRl3Ht4JOcdU872GhiYWf6jwrk_SNhz9

Deep Learning with Python https://www.amazon.com/dp/1617294438/ref=cm_sw_r_cp_apa_i_9YmTCbSDTWXB7

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

Andrew Ng's Machine Learning Online Course

Deep Learning with Python Book by Francois Chollet

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Bonus Gift:

Manga Guide to Regression Analysis

Yes. I'm a developer and I started learning ML through fastai's course "Practical Deep Learning for Coders, v2". It was an excellent introduction. It does presume you have previous programming experience, however.

I also really benefitted from Francoise Chollet's book. Link One of the big barriers to learning this stuff is that it tends to be taught by mathematicians. I'm not a mathematician so if you show me the backprop algo as a formula I just glaze over. Francoise Chollet explains it all as a set of python functions, which is very readable. If you are more mathematically inclined, you might not like that approach.

So much of data science is going to involve programming, theres really no way to avoid learning it first. You have to clean your data, move variables around, join tables and whatnot. Step 1 is definitely learn python. Fastai is a good step 2.

Io avevo iniziato con questo libro sul deep learning scritto dal creatore di Keras.
Ti consiglio anche di vedere le sfide su Kaggle!

Chiedo visto che sono interessato anche io: Ci sono gruppi/meet-up a Roma e dintorni per appassionati Machine learning?

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.

Awesome list! I'm a software engineer looking to make the jump over to data science, so I'm just getting my feet wet in this world. Many of these books were already on my radar, and I love your summaries to these!

One question: how much is R favored over Python in practical settings? This is just based off of my own observation, but it seems to me that R is the preferred language for "pure" data scientists, while Python is a more sought-after language from hiring managers due to its general adaptability to a variety of software and data engineering tasks. I noticed that Francois Chollett also as a book called Deep Learning with Python , which looks to have a near identical description as the Deep Learning with R book, and they were released around the same time. I think its the same material just translated for Python, and was more interested in going this route. Thoughts?

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I'm a big fan of the 'Deep Learning With Python' book by Francois Chollet.

https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438

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Looks like the whole book is available here but the link is .cn so check it out on your own.

http://faculty.neu.edu.cn/yury/AAI/Textbook/Deep%20Learning%20with%20Python.pdf