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hettlage (136) [Avatar] Offline
#1
Here are some suggestions for chapter 3. (I hope my remarks don't sound too negative!)

  • The aim of the book is to teach machine learning. But then, I think, it should explain the algorithms used. Hence just providing example code with the GrdientDescentOptimizer function is insufficient - you should actually describe what this function is doing. (I'm not saying you should provide all the gory mathematical details, but you should at least give the qualitative picture - and maybe a link to the details). In other words, you shouldn't (only) provide cookbook recipes, but give the reader an understanding of why these work.


  • You should explain the meaning of the learning rate and learning epochs (and how to choose reasonable values for them).


  • One thing which eluded me until the very end of the chapter (when I stumbled across the reduce_sum function and had to find out its meaning [which you might want to explain]) was that the model and cost actually operate on vectors, not scalars. It might be worth emphasizing this fact. Going through the algorithm of the Python code with mathematical formulae might help. (It might also help to expand on placeholders in chapter 2 (as in http://learningtensorflow.com/lesson4/).)


  • The main text and the Python code differ regarding the cost function for regularisation. Also, the statement "The choice of norm depends case by case" leaves the reader (or at least this reader) wondering how to figure out which norm to choose.


  • Maybe a plot of final_cost over lambda instead of (or in addition to) Figure 3.11 might be instructive, in particular if it includes an explanation of why it looks the way it does.


  • You might expand on the concept/definition of "bias" and "variance" in section 3.1.1.


  • You start chapter 3.1 with the statement "If you have a hammer, every problem looks like a nail." In this spirit, it might add value to the chapter if you give an example or two of where linear regression is *not* the appropriate tool - not everything is a nail, after all. smilie


  • You refer to real data at the end of the chapter. But perhaps it might be instructive if you actually include a real world example in the chapter.


  • I'm not 100 % convinced that using linear regression as the first example for a TensorFlow program is the ideal choice. After all (although this might be subjective) fitting a polynomial to a set of data points isn't the most exciting thing ever. The TensorFlow website starts off by interpreting handwritten digits, whicvh looks more interesting to me. I'm not suggesting to change the order of chapters (linear regression is a reasonable starting point, I guess), but maybe you could include a "teaser" (like the one on the TensorFlow site) at the end of chapter 2.
  • 441856 (2) [Avatar] Offline
    #2
    I would like to suggest something during my learning:
    + I had to go through Andrew Ng course on coursera.org to understand deeper about Linear Regression. Your explanation is still so abstract regarding the goal of Linear Regression and how it can turn out result. that would be awesome when you explain more detail about Cost Function regarding its goal and our intention to the end. I felt like you go through Tensorflow very fast without explaining about formula.

    + There are lack of math explanations: I have read a lot document to approach your tensorflow examples. Or somehow your explanation is not radical to understand how every single dataset fertilize the result.
    456564 (1) [Avatar] Offline
    #3
    +1

    Yes, I had the same feeling as mentioned in the first post.

    Sometimes it feels rushed through the examples without explaining the details.
    Nishant Shukla (52) [Avatar] Offline
    #4
    Chapter 3 is now revamped with careful explanations and effective visuals! Enjoy!