list-of-figures

List of Figures

 

Chapter 1. What is deep learning?

Figure 1.1. Artificial intelligence, machine learning, and deep learning

Figure 1.2. Machine learning: a new programming paradigm

Figure 1.3. Some sample data

Figure 1.4. Coordinate change

Figure 1.5. A deep neural network for digit classification

Figure 1.6. Deep representations learned by a digit-classification model

Figure 1.7. A neural network is parameterized by its weights.

Figure 1.8. A loss function measures the quality of the network’s output.

Figure 1.9. The loss score is used as a feedback signal to adjust the weights.

Figure 1.10. A decision boundary

Figure 1.11. A decision tree: the parameters that are learned are the questions about the data. A question could be, for instance, “Is coefficient 2 in the data greater than 3.5?”

Chapter 2. Before we begin: the mathematical building blocks of neural networks

Figure 2.1. MNIST sample digits

Figure 2.2. The fifth sample in our dataset

Figure 2.3. A 3D timeseries data tensor

Figure 2.4. A 4D image data tensor (channels-first convention)

Figure 2.5. Matrix dot-product box diagram

Figure 2.6. A point in a 2D space

Figure 2.7. A point in a 2D space pictured as an arrow

Figure 2.8. Geometric interpretation of the sum of two vectors

Figure 2.9. Uncrumpling a complicated manifold of data

Figure 2.10. Derivative of f in p

Figure 2.11. SGD down a 1D loss curve (one learnable parameter)

Figure 2.12. Gradient descent down a 2D loss surface (two learnable parameters)