Hands-On Machine Learning (3rd ed) #59
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另外一本更基础的书是《Python机器学习基础教程》 |
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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (3rd ed)
Machine Learning Notebooks
Welcome to the Machine Learning Notebooks!
Code Examples
All the code examples in this book are open source and available online at https://github.com/ageron/handson-ml3, as Jupyter notebooks.
Prerequisites (see below)
Notebooks
Scientific Python tutorials
Math Tutorials
Linear Algebra
Differential Calculus
Extra Material
Auto-differentiation
Misc.
Equations (list of equations in the book)
Prerequisites
To understand Python – you don't need to be an expert python programmer, but you do need to know the basics. If you don't, the official Python tutorial is a good place to start.
Scientific Python – We will be using a few popular python libraries, in particular NumPy, matplotlib and pandas. If you are not familiar with these libraries, you should probably start by going through the tutorials in the Tools section (especially NumPy).
Math – We will also use some notions of Linear Algebra, Calculus, Statistics and Probability theory. You should be able to follow along if you learned these in the past as it won't be very advanced, but if you don't know about these topics or you need a refresher then go through the appropriate introduction in the Math section.
To run the examples
Jupyter – These notebooks are based on Jupyter. You can run these notebooks in just one click using a hosted platform such as Binder, Deepnote or Colaboratory (no installation required), or you can just view them using Jupyter.org's viewer, or you can install everything on your machine, as you prefer. Check out the home page for more details.
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