Hands-On Convolutional Neural Networks with TensorFlow

Old versus new ML

The typical flow that an ML engineer might follow to develop a prediction model is as follows:

1. Gather data
2. Extract relevant features from the data
3. Choose an ML architecture (CNN, ANN, SVM, decision trees, and so on)
4. Train the model
5. Evaluate the model and repeat steps 3 to 5 until they find a satisfying solution
6. Test the model in the field

As mentioned in the previous section, the idea of ML is to have an algorithm that is flexible enough to learn the underlying process behind the data. This being said, many classic methods of ML are not strong enough to learn directly from data; they need to somehow prepare the data before using those algorithms.

We briefly mentioned it before, but this process of preparing the data is ...