What is Reddit's opinion of Deep Learning (Adaptive Computation and Machine Learning series)?

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Well then I'm sorry, cheers and thanks for the link! I'm looking for something to read after "finishing" this one.

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.

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/

Un placer ayudar

1. Depende da lo que quieras complementar, si estudiaste matemáticas entonces tienes que aprender software. Si estudiaste informática probablemente tienes que estudiar matemáticas. Hay muchos cursos online, es cosa de hacer una buena investigación de los materiales mejor votados y comprometerse con uno.
2. Yo en estos momentos estoy en un puesto intermedio entre sr y technical manager. Esto dividiendo mi tiempo entre resolver los requerimientos de la gente de negocios, liderando equipos, programando un poco y diseñando arquitecturas. Depende mucho de la empresa en donde trabajes qué peso le va a dar a cada cosa. La exigencia laboral es cada vez mas alta. Yo no tengo una semana donde no sienta que empujé mi entendimiento, nunca se vuelve plano, en mi experiencia. Jr: estas en el codigo, semi senior: estas en el codigo de forma mas independiente, sr: estas diseñando soluciones, lideando con el negocio, con gente de productos, diseñando arquitecturas y liderando equipos.
3. Esta pregunta es muy amplia. Pero en resumen el nivel de proactividad y atención a los detalles.
4. Siempre parto por articulos en medium.com, despues me paso a leer algun paper, pero la verdad es que ultimamente no esoy leyendo mucho paper. Es tan loca la exigencia que uno tiene que estar aprendiendo como aplicar miles de cosas al mismo tiempo, en vez de entender los fundamentos, lamentablemente. Para entender papers de ML/DL hay que estar solido en calculo, algebra, estadística, probabilidades y todo lo que es la teoría de ML en general.
5. Las de coursera estan buenas, pero lo que tiene real peso son las certificaciones directas de esos proveedores cloud. Por ejemplo ML engineer certication de Google (vale como 200 usd masomenos), igual hay que saber un montón para esto, no es para nada inicial.
6. Bien. El mundo esta cada vez más digitalizado (especialmente post pandemia), la digitalizacion trae datos, los datos traen ingeniería de datos, y eso lleva eventualmente a la capacidad de generar productos de anaítica avanzada. Por otro lado, aunque no este bueno en Chile, si tu inglés es bueno, puedes trabajar en cualquier parte del mundo sin problemas.
7. Lo más fuerte seria un MBA en data science, pero son carísimos. La forma que yo hice fue leer muchos casos de uso en blogs de google, amazon, ibm, microsoft y buscar frases clave como "ML for recommendation systems in retail". Aqui hay que leer muhos casos de uso y pregutnar en subreddits del área.
8. De machine learning: https://www.statlearning.com/. De deep learning: Este . Igual un disclaimer, no estoy al tanto de los ultimos materiales que han salido, asi queno te fies de estos libros, probablemente hay mejores, pero seguro estos no te van a descepcionar.

Te respondi todo viste ;)

Espero que te sirva.

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

Amazon, who's not always reliable, claims December 9th : https://smile.amazon.com/dp/0262035618/

That is indeed more of a surface-level book to provide you with some context (it's definitely not a book to learn ML from). The classical book many people learn ML from is this - https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618

Depends on so many factors. Are you just looking to write deep learning algorithms or are you actually interested in research + theory? What sort of math are you comfortable with? Can you read & write proofs?

You're probably going to see the book "Deep Learning" by Goodfellow et al mentioned here or elsewhere. Keep in mind it's pretty math-heavy and probably won't really help you write code. You'll need to know calculus and at least linear algebra. That being said it's a great book if you're looking for theory and math.

Honestly, if you're not comfortable with multivariable calculus and linear algebra I wouldn't suggest you start learning deep learning at all until those are familiar to you. It would also behoove you to have a solid foundation in mathematical statistics and probability.

Assuming you have the necessary background in mathematics I would recommend the following:

Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig will provide a broad overview of the theory and practice of artificial intelligence generally.

The Elements of Statistical Learning would be a good place to go from there.

The aforementioned Deep Learning book by Goodfellow et al.

After those look for something practical. Someone here mentioned a book they wrote. Always a good idea to support the Reddit community. There's also:

Hands-On Machine Learning

Deep Learning with Python

Grokking Deep Learning

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.

free online: https://www.deeplearningbook.org/

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

or elsewhere: https://en.wikipedia.org/wiki/Special:BookSources?isbn=978-0262035613

This one has math fundamentals and in fact every model has underlying math formulas and in accompanied with code for Mxnet, Pytorch and Tensorflow. I like the way the authors combined theory with practice, you are getting best of both worlds. There are also notebooks on every model. http://d2l.ai/

Another good math heavy read is Deep Learning. From Goodfellow et al. https://www.amazon.de/dp/0262035618/ref=as_li_tl?ie=UTF8&linkCode=gs2&linkId=00701393c949f16bfd3a89d9c3240b35&creativeASIN=0262035618&tag=petacrunch07-21&creative=9325&camp=1789

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

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

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

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https://www.deeplearningbook.org/

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

Simon Goodfellow's book is a really good resource for deep learning.

https://www.amazon.com/dp/0262035618/ref=cm_sw_r_cp_awdb_t1_rVmvBbJ0DGFKH

And Data Mining gives a good overview of a variety of algorithms.

https://www.amazon.com/dp/0128042915/ref=cm_sw_r_cp_awdb_t1_0XmvBbCAH9Q77

I think the name of the book is literally Deep Learning, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.

Deep Learning (Adaptive Computation and Machine Learning series)