Finished 18/07/2022
Hands-on Machine Learning with Scikit-Learn and TensorFlow
Good read, it’s a classic; it covered all the main points, building up progressively and providing plenty of code. I might reread it since the final TensorFlow section was fairly challenging.
Notes
- Python and statistics basics: fine, clear, especially when it covers MSE and the usual metrics; I skipped the Python parts
- Exploratory analysis was interesting; I learned a few things about matplotlib
- Data cleaning: fine, the usual pandas, while PCA felt tedious and not very useful to me
- Very useful information on encoding, dummy variables, and the importance of standardization
- Regression: the usual topics, though the focus on the math was a nice addition
- SVMs: I have not used them yet
- Decision Trees and Random Forests: very good; it also covered other ensemble models, which seem the most interesting in terms of performance
- Excellent section exploring TensorFlow, especially the math behind functions and a lot of tweaking
- Perceptron and basic concepts: fine; I could follow along
- CNNs were already quite challenging, but the tweaking and the explanation of hyperparameters were excellent
- RNNs: fine, very focused on translation; I believe LSTMs are included
- The autoencoder section was new to me; I’m not sure they are still widely used to generate new material now that there are ANNs
- The finale on reinforcement learning was very interesting but challenging, as it had a lot of code.
Not an easy read, but I will definitely reuse the code if I use TensorFlow in a more advanced way; for now I have not trained anything.