Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems PDF, 2nd Edition. This book assumes you know next to nothing about machine learning. Its goal is to provide you with the concepts, tools, and insights you need to implement programs that can learn from data. We'll cover a large number of techniques, from the simplest and most commonly used (like linear regression) to some of the deep learning techniques that regularly win competitions.
So of course you are enthusiastic about machine learning and would like to take part! Perhaps you would like to give your self-made robot its own brain? Let him recognize faces? Or teach him to walk? Or maybe your company has tons of data (user logs, financial data, manufacturing data, machine sensor data, hotline statistics, human resources reports, etc.) and you could probably discover some hidden gems if you only knew where. With machine learning, you can:
Instead of implementing our own toy versions of each algorithm, we use production-ready Python frameworks:
Scikit-Learn is very easy to use, but it implements many machine learning algorithms efficiently, so it is a good starting point for learning machine learning.
TensorFlow is a more complex library for distributed numerical computing. It enables you to efficiently train and operate very large neural networks by distributing computations across potentially hundreds of multi-GPU servers. TensorFlow was developed by Google and supports many of its extensive applications. It has been open source since November 2015; version 2.0 was released in October 2019.
Keras is a high-level deep learning API that makes it easy to train and operate neural networks. It can run on TensorFlow, Theano, or Microsoft Cognitive Toolkit (formerly known as CNTK). TensorFlow comes with its own implementation of this API called tf.keras that supports some advanced TensorFlow features (such as the ability to load data efficiently).
Aurélien Géron is a machine learning consultant and trainer. As a former Googler, he led the YouTube video review team from 2013 to 2016. He was also the founder and CTO of Wifirst (a leading wireless ISP in France) from 2002 to 2012 and the founder and CTO of two consulting firms: Polyconseil (telecommunications). , Media and strategy) and Kiwisoft (machine learning and data protection).
Right now I'm only in the first few chapters, but I noticed that it is very well written. It helps that it's a second edition. The concepts of ML are not easy to explain clearly, so the author has done a great job. The second part of Deep Learning looks completely new for the second edition. I was mostly interested in deep learning and wanted to skip part 1, but glad I didn't. Part 1 gives me a brief overview of basic machine learning.
As for the Kindle version, I'm happy to announce that the formatting works just fine. The code is easy to read and the math formulas are displayed nicely. There is also color in the charts. Books printed by O'Reilly are only black and white and some graphics cannot be read because all the lines or dots are the same black color. No problem here. I managed to get Alexa to read the book to me in a very realistic voice. I used iPad Pro, installed the Alexa app on it, then opened the Kindle book from the Alexa app, selected the chapter and hit play. Alexa correctly pronounced scikit-learn what a result is. The vocal engine looks vastly improved and sounds very good. A Bluetooth headset is connected. Forward, backward, pause and resume buttons on the headset work.
I'm just starting with Chapter 4, but if the rest of the book is of the same quality as the first chapters, it definitely deserves 5 stars. The book title covers the content, and the book is packed with practical tips and working code examples. In fact, it comes with complete projects in the form of Jupyter notebooks. You definitely can't go wrong with the purchase of this book given the price. Even if there are some chapters at the end that you don't like, it's worth it
I've been using the ML learning box for a while now. I've bought books in the past but nothing really stuck because they go crazy from page 4 onwards. This book requires very little knowledge of ML, which describes me perfectly. I bought this book and can't put it down. I'm on page 47 and keep looking to see what's next. The best reference book I have bought in many years. When I look at all of the content, I am amazed at how anyone can present ALL of this information in a format I can get. I love it, I absolutely recommend it. Thank you for this.
If you want to learn more about data science, I highly recommend this book. The only requirement is that if you haven't done math or statistics for a few years, read "Practical Statistics by Data Scientist" first, or you might get lost a little. While this is an introduction to machine learning, it is not an introduction to Python, linear algebra, calculus, or statistics. You can still follow the code and scientific explanations, but if you are new to math or programming you should spend a few hours researching a single paragraph from this book. It is not bad, indeed it is one of the strengths of this book. Once you understand the basic concepts, the author will show you briefly and accurately how to implement the model.
This book is a gem! I have read a lot of textbooks and this is one of those books that is interesting, informative, well structured and has so many details. The book is more intended for the professional, but there is still enough math and plenty of paper references if the reader wants more theoretical information. I would recommend this book to anyone, whether you are a graduate or PhD student with several years of experience. A really well written book! Thanks Aurelio!
The book begins with a brief general discussion of machine learning, including data preparation, visualization, breaking down train and test sets, model fitting, and evaluation.
Most of the book focuses on the techniques that represent the state of the art: ensembles and especially deep learning. There is enough math to be convincing, but not so much to detract from practical applications. The sections describing deep learning architectures are particularly well done. Throughout the book, clear illustrations and fully exposed Python 3 code allow the reader to replicate the author's work.
I completed a master's degree in machine learning as part of my master's. I had an excellent conference paper. The professor used the first edition of this book as a textbook for the course. I had a first edition of the book but didn't have time to read. I am now purchasing the second edition because Tensorflow 2 is merged with Keras which means we can avoid learning the rigid syntax of Tensorflow 1.0 and there are many new advances in machine learning such as generative models. To my surprise, the book is also colorful. This makes the book more interesting.
Each chapter has a summary of the mathematics. It's better than some machine learning programming books that don't have math. If you have prior knowledge of machine learning math, this book can save you time as it will give you the big picture without loss. If you are very interested in some equations and want to derive them, you can use the Pattern Recognition and Machine Learning book.
The github has many Python machine learning projects. The codes are well written. If you can write code like codes in projects, you have the option to type google. Go to Google, the book is a must.
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