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Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems
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Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.
The updated edition of this best-selling book uses concrete examples, minimal theory, and two production-ready Pyth ...more
The updated edition of this best-selling book uses concrete examples, minimal theory, and two production-ready Pyth ...more
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Start your review of Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems
I spent the last five months learning the math and theory behind machine learning, but when I finally tried to do something on a simple Kaggle set, I was drawing blanks.
This book really showed me what I was missing: context. It doesn't just demonstrate different tools, it gives you a framework that you can apply to any problem (chapter 2) and how to think about what you're doing in each phase of an ML project.
It doesn't baby you on the math, but it doesn't go deeper than it needs to either. I ...more
This book really showed me what I was missing: context. It doesn't just demonstrate different tools, it gives you a framework that you can apply to any problem (chapter 2) and how to think about what you're doing in each phase of an ML project.
It doesn't baby you on the math, but it doesn't go deeper than it needs to either. I ...more
I thought this book was a great overview of the actual practice of machine learning, and I think it compares favorably to something like Andrew Ng's "Machine Learning Yearning" which contains significantly less detail and no code. In general I think this was a pretty good beginning resource for using these frameworks, and it ends up feeling like reading structured documentation. I think this is a particularly useful part of this book, since in my experience a lot of this field is knowing which s
...more
The best book about the subject out there. It contains easy to understand code in Python and covers from simple linear regression to RNN and CNNs that were published a few months before the launching of the book. A must have.
We are reading this book for the book club at Synopsys. We are pretty far in and I am scheduled to present Chapter 7 next week.
This is a very practical overview of machine learning techniques using Python. The first couple books I read on it included stuff like "how to design machine learning algorithms". This book is more about showing the huge volume of algorithms that are already developed and easy to access. Pretty much every optimization and tweak you might think of is there, you just need ...more
This is a very practical overview of machine learning techniques using Python. The first couple books I read on it included stuff like "how to design machine learning algorithms". This book is more about showing the huge volume of algorithms that are already developed and easy to access. Pretty much every optimization and tweak you might think of is there, you just need ...more
In my opinion, this is still the best technical book about ML and AI, I've read so far. The balance between theory and practice suits me very well.
Nevertheless, this is not for beginner. You must be confident to read and code in Python also with main scientific libraries like numpy, matplotlib. More importantly, good mathematics knowledge is important, so it's better to review linear algebra, calculus, probability theory and statistics beforehand.
The book consists of two parts:
1. Machine Learnin ...more
Nevertheless, this is not for beginner. You must be confident to read and code in Python also with main scientific libraries like numpy, matplotlib. More importantly, good mathematics knowledge is important, so it's better to review linear algebra, calculus, probability theory and statistics beforehand.
The book consists of two parts:
1. Machine Learnin ...more
This book was a fantastic surface-level introduction to a vast array of machine learning methods, including their implementation in Scikit-Learn, Keras and Tensorflow (2.0). It's written in a casual style, which makes the flow a lot better compared to terse textbooks. The newest version also covers new concepts such as the Transformer architecture for natural language processing, as well as Generative Adversarial Networks and reinforcement learning.
Sometimes the author gets a bit bogged down on ...more
Sometimes the author gets a bit bogged down on ...more
I want to rate this almost 4.5 stars :)
Disclaimer: I have read the Scikit-Learn portion in full, and the Keras and Tensorflow portion through Convolutional Neural Networks, which I am using in an imaging project.
The author crams a lot of material in a short space and expects you to pick up *all* that he's putting down. Even though this takes a more hands-on approach as compared to theoretical juggernauts, the author presents enough theory to ground the practical aspects.
A lot of the tips and tri ...more
Disclaimer: I have read the Scikit-Learn portion in full, and the Keras and Tensorflow portion through Convolutional Neural Networks, which I am using in an imaging project.
The author crams a lot of material in a short space and expects you to pick up *all* that he's putting down. Even though this takes a more hands-on approach as compared to theoretical juggernauts, the author presents enough theory to ground the practical aspects.
A lot of the tips and tri ...more
This is the first time I review a ... kind of like a textbook.
When I was in my second year in University, I decided to learn Machine Learning, and every page suggested this book. Nevertheless I bought it, and it turns out the book is super helpful. You get your hands on real projects, data, you build real applications, you use the model to run on your own dataset. The book covers most of the fields in ML, from traditional supervised and unsupervised learning to deep learning like Neural network, ...more
When I was in my second year in University, I decided to learn Machine Learning, and every page suggested this book. Nevertheless I bought it, and it turns out the book is super helpful. You get your hands on real projects, data, you build real applications, you use the model to run on your own dataset. The book covers most of the fields in ML, from traditional supervised and unsupervised learning to deep learning like Neural network, ...more
Nov 30, 2020
Albert
added it
I can't say that this is for beginners. I've come up to regression chapter and finished it, I copied the code and make it run but I can't say that I understand all the information provided. I'm a programmer and still didn't get lots of ideas.
And when I tried the Coursera online course, and finished the first course out of 4. I've understand it very well. It explains little by little the concept of Machine Learning.
And after that I come back to this book and things makes sense now.
So for those be ...more
And when I tried the Coursera online course, and finished the first course out of 4. I've understand it very well. It explains little by little the concept of Machine Learning.
And after that I come back to this book and things makes sense now.
So for those be ...more
The book gives a pretty good overview of how to use Scikit-Learn and Keras/TensorFlow. There is math providing the details behind some of the black boxes. Also, you don't need to be much of an expert at Python to be able to follow the code and use the examples.
One annoying thing about the book: the author loves the word "simply". To do anything, "you simply do this" or "you simply do that." It gets rather tedious after you read that word over and over. I think, for the next edition, the author ...more
One annoying thing about the book: the author loves the word "simply". To do anything, "you simply do this" or "you simply do that." It gets rather tedious after you read that word over and over. I think, for the next edition, the author ...more
Because I started Master Degree in Data Science I made some research for a good book, Hands-On ML was one of books in list but I start with Deep Learning with Python since main language in ML courses in Master was Python, and read a couple more but really no finish left on beginning to be sincere I really enjoy Hands-On over the other books I read and really recommend it to those who want to start in ML world and understand how it works.
Great book on deep learning and other machine learning methods. Clearly written and provides many intuitive explanations, examples, and codes. The Deep learning part covers some latest papers such as Attention is All You Need (2017) and StyleGan (2018). I find the logic behind the tensorflow data pipeline grammar (chap 16) hard to grap and wish the author could explain more, but it could be just me.
In effect, the second edition of this volume, expanded to 800 pages, now including Keras, TensorFlow 2, unsupervised learning, updated neural network architectures, and a whole lot more. The explanations are mostly clear, with the use of Keras making a huge improvement over the previous volume's focus on TensorFlow. It's a hugely impressive piece of work. Recommended!
...more
Very comprehensive
This book finally got me started on machine learning - something I have not managed with a lot of other resources. It covers a lot of ground, both in theory and the practical application. Recommended. Just make sure that you do not buy the Kindle version, as others pointed out (I bought the hardcover).
This book finally got me started on machine learning - something I have not managed with a lot of other resources. It covers a lot of ground, both in theory and the practical application. Recommended. Just make sure that you do not buy the Kindle version, as others pointed out (I bought the hardcover).
A superb go to supplement book with the theoretical lectures from Andrew Ng. If you are already comfortable with the theory, this book is handy for doing the practical hands-on approach. It first focuses on Machine Learning using scikit learn right from framing a problem then focuses on Deep Learning with Keras and Tensorflow. Highly recommended book.
This is not a textbook, it's a code repository.This book doesn't explain how the models work just teaches you how to code the model, it just gives you some equations and tries to explain them in a few lines.
...more
An awesome ML book for beginners to have more hands-on experience in Machine Learning and Deep Learning.
There're some tricks and best practices inside this book that can be the secret weapon to your model.
I highly recommend this book for programmers who want to taste the favor of ML and DL. ...more
There're some tricks and best practices inside this book that can be the secret weapon to your model.
I highly recommend this book for programmers who want to taste the favor of ML and DL. ...more
This is an absolute banger of a book. Excellent balance between putting the algorithms on practice, what’s happening under the hood, and important parameters.
It is a must read if you’re getting into ML.
It is a must read if you’re getting into ML.
I was in for a treat!! Completely blown away from the beginning. I think this is one of the most interesting text books I've ever read. Can be recommended for all types of machine learning learners since it has lessons that start from ML 101 to more complex and sophisticated topics at the end.
...more
I was looking for such book a long time since I started to learn Machine Learning. It is very broad and useful in its scope. Book examines traditional machine learning algorithms as well as Artificial Neural Networks. Some summary to popular algorithms with flawless visualization techniques.
Superb book on ML. It covers complete machine learning from regression using Scikit. Although make sure you have certain understanding of python and college level mathematics before reading this book. I highly recommend this book.
The best book about ML. Great readability and writing style with many interesting touches.
Amazing book! Great explanations and nice visualization. I will probably keep rereading it as needed
A great collection of all you need to start using more advanced machine learning packages
Not for beginners, but absolutely complete
This is a long good book to keep for reference.
But I think that exist more easy approaches on the subject.
But I think that exist more easy approaches on the subject.
Great for beginners in machine learning with a lot of examples. If you are scared of math, it is reduced with just a few equations and a lot of explainantions.
One of the best for ML...
If you already have basic knowledge of calculus, linear algebra, statistics, and programming with Python, this book is just the best for machine learning applications.
One of the best machine learning Python resources out there. Highly recommended.
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