Chapter 1. A machine-learning odyssey
Figure 1.1. Each pair of integers, when summed, results in an even or odd number. The input and output correspondences listed are called the ground-truth dataset.
Figure 1.2. This table reveals the inner logic behind how the output response corresponds to the input pairs.
Figure 1.3. An ML approach to solving problems can be thought of as tuning the parameters of a black box until it produces satisfactory results.
Figure 1.4. The learning approach generally follows a structured recipe. First, the dataset needs to be transformed into a representation, most often a list of features, which can be used by the learning algorithm. The learning algorithm chooses a model and efficiently searches for the model’s parameters.
Figure 1.5. The inference approach generally uses a model that has already been either learned or given. After converting data into a usable representation, such as a feature vector, it uses the model to produce intended output.
Figure 1.6. Feature engineering is the process of selecting relevant features for the task.
Figure 1.7. The L1 distance is also called the Manhattan distance (also referred to as the taxicab metric), because it resembles the route of a car in a grid-like neighborhood such as Manhattan. If a car is traveling from point (0,1) to point (1,0), the shortest route requires a length of 2 units.