Thoughtful Machine Learing with Python Chapter 1



This chapter is called Probably Approximately Correct Software.
The first section is called Writing Software Right.  It starts by explaining SOLID principles
  1. Single Responsibility Principle
  2. Open/Close Principle
  3. Liskov Substitution Principle
  4. Interface Segregation Principle
  5. Dependency Inversion Principle
Each of these sections is well defined, and gives a good overview of what each of these mean.

The next section is Testing or TDD.  Unless you've been under a rock, the last few years, you probably already know what TDD (test driven development) is in software engineering.  If you have never heard of TDD, now is a good time to pick up this book, and learn more about it.

Refactoring is next, and the author, again, does a good job of  explaining this concept to a "new" developer.  Again, if you know what is being talked about, this whole section can be skipped.

The last major section of this chapter is called Writing the Right Software.  The author goes on to give an example of a company that wrote great software, but still failed miserably.  The entire point boils down to how can we write the Right Software? Mr. Kirk then gives a brief overview of what exactly is machine learning.


The last major section goes over the SOLID principles, and applies them to machine learning problems. 

All in all, this was a pretty decent chapter.  I thought some of it was a little basic for developers who care about machine learning, but I appreciated not slogging through a chapter on how to install python, and the various libraries. 


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