Eryk Banatt's Reviews > Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems

Hands-On Machine Learning with Scikit-Learn, Keras, and Tenso... by Aurélien Géron
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really liked it

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 search terms to use - a task I think this book accomplishes rather neatly for its reader.

I read through this book but I expect I will probably revisit it (along with the notes I took on it) when I need to actually do something it mentions. For example, I don't currently have any need for deploying a tensorflow model but I'm certain eventually it will be something I will need to know how to do. It's a nice reference point: I now vaguely know what to do for that, and know I have a good summary + examples available to me when I need it in the future.

I did find that a number of the chapters were a little overly repetitive, but I think that's largely because I'm not exactly a beginner to keras, so I'm willing to not pay any mind to this. But for people who use some of these frameworks relatively often it can get a little boring to hear "if you want to make a custom version of , you can subclass the existing feature" for every feature.

That said, in general I found that this book captured a nice balance between exposition and documentation. Especially something with as much wide functionality as scikit-learn, it can sometimes get to be a bit much to list out every technique in every module the way it might be in the actual sklearn documentation. However, Aurelien manages to pick out a few examples that capture the essence of the things you need to know, such that even if you've never seen something before you can easily figure out what's happening (i.e. intoducing sklearn.manifold.TSNE after not mentioning it, but mentioning sklearn.manifold.locally_linear_embedding; you might not really know what TSNE is if you've never heard it before (if you live under a rock), but you can probably easily infer from context what it's supposed to be doing). You don't need to know every single classifier available in something like sklearn, all you really need to know is that they're all mostly drop-in replacements for each other, and you can look up the details later in the actual documentation if you forget.

Likewise, tensorflow 2 really just seems like keras 2, so I'm pretty excited about playing around with it a bit more. My opinions on the actual framework are probably not super within the scope of this book, though, so I won't bother detailing them here.

In general, a pretty good intro for beginners, and fairly easy to get through if you just want to know how to use a framework you previously didn't know how to use (like tf 2.0)
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Finished Reading
October 27, 2019 – Shelved

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