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Machine Learning with Tensorflow
Being able to make near-real-time decisions is becoming increasingly crucial. To succeed, we need machine learning systems that can turn massive amounts of data into valuable insights. But when you're just starting out in the data science field, how do you get started creating machine learning applications? The answer is TensorFlow, a new open source machine learning library from Google. The TensorFlow library can take your high level designs and turn them into the low level mathematical operations required by machine learning algorithms.
Machine Learning with TensorFlow teaches readers about machine learning algorithms and how to implement solutions with TensorFlow. It starts with an overview of machine learning concepts and moves on to the essentials needed to begin using TensorFlow. Each chapter zooms into a prominent example of machine learning. Readers can cover them all to master the basics or skip around to cater to their needs. By the end of this book, readers will be able to solve classification, clustering, regression, and prediction problems in the real world.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
Machine Learning with TensorFlow teaches readers about machine learning algorithms and how to implement solutions with TensorFlow. It starts with an overview of machine learning concepts and moves on to the essentials needed to begin using TensorFlow. Each chapter zooms into a prominent example of machine learning. Readers can cover them all to master the basics or skip around to cater to their needs. By the end of this book, readers will be able to solve classification, clustering, regression, and prediction problems in the real world.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
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Published January 1, 2017
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Displaying 1 - 6 of 6 reviews
October 23, 2019
Some context first:
I have some experienced with ML, I've had hands-on practice with supervised & unsupervised learning, even some basic NN, BUT using R & Octave, not Python. So my intention was to catch up on Python tool-set, especially TensorFlow & Jupyter to apply the previously acquired knowledge. It all made sense, because 2019 is the time of the so-called 2nd wave of ML - algorithms & basic methods are the well standardized & encapsulated in libraries & frameworks that if you know the applicability & basic idiomatic constructs, you don't even have to know all the math - you can treat it like a black-box.
But it didn't work with this book at all. It's actually quite good when describing the methods - where & when to use them, what are good examples of usage, etc. It also provides ready-to-use (?) examples in Python & TensorFlow. So what's the problem? Well, it fails in-between. It fails in presenting the foundation concepts of TF - what are its building blocks, how to use them (& how NOT to use them). What you get is an example, tagged with 5-7 comments & ... that's about it. Just go figure. Sure, I can do it, but isn't this a whole purpose? To build up the understanding of how does the example work so next time I can independently craft another, similar example?
In the end I was just irritated & I've read 85% of truly useful stuff on TF on-line docs - I don't feel the book was in any way substantial in my learning process.
I have some experienced with ML, I've had hands-on practice with supervised & unsupervised learning, even some basic NN, BUT using R & Octave, not Python. So my intention was to catch up on Python tool-set, especially TensorFlow & Jupyter to apply the previously acquired knowledge. It all made sense, because 2019 is the time of the so-called 2nd wave of ML - algorithms & basic methods are the well standardized & encapsulated in libraries & frameworks that if you know the applicability & basic idiomatic constructs, you don't even have to know all the math - you can treat it like a black-box.
But it didn't work with this book at all. It's actually quite good when describing the methods - where & when to use them, what are good examples of usage, etc. It also provides ready-to-use (?) examples in Python & TensorFlow. So what's the problem? Well, it fails in-between. It fails in presenting the foundation concepts of TF - what are its building blocks, how to use them (& how NOT to use them). What you get is an example, tagged with 5-7 comments & ... that's about it. Just go figure. Sure, I can do it, but isn't this a whole purpose? To build up the understanding of how does the example work so next time I can independently craft another, similar example?
In the end I was just irritated & I've read 85% of truly useful stuff on TF on-line docs - I don't feel the book was in any way substantial in my learning process.
July 21, 2019
There tends to be a lot of repetition between various machine learning books which essentially cover a lot of the same topics. This makes a lot of the content of most books skippable since you have seen it before. This is also true of this book where a lot of topics concerning the basics of machine learning are repeated and a lot of models described in more detail than necessary since you can find them described better in lecture notes or machine learning textbooks. The book also has a lot of repetition in the code samples like a lot of Manning titles. Good for what it is but perhaps quite a bit longer than it needs to be.
March 7, 2019
Prompts a brief explanation to Machine Learning with Tensorflow. Nishant describes Tensorflow as "the auto-focus" for machine learning. And his analogy is completely mind-shifting, because of the topics he introduces are the foundations of machine learning. I completely detoured from completing each exercise to just reading for intuition by the end of this book.
March 7, 2018
It's not exhaustive but a good start.
January 14, 2020
Good for Beginners
February 21, 2018
a decent reference book for solving various problems using tensorflow. However, having read this book, I do not feel I know tensorflow well enough to use it without external help and I neither feel more enlightened about machine learning. I think it was especially not impressive due to the fact I had just read "Deep learning with Python" just before, which is an outstanding book.
Displaying 1 - 6 of 6 reviews