3.1.3. Loss functions and optimizers: keys to configuring the learning process

3.1.1. Layers: the Lego bricks of deep learning

3.3.5. What is the best GPU for deep learning?

3.3.4. Running deep learning jobs in the cloud: pros and cons

3.3.3. Getting Keras running: two options

3.3.2. Jupyter notebooks: the prefered way to run deep learning experiments

3.4.5. Using a trained network to generate predictions on new data

3.5. Classifying newswires: a multi-class classification example

3.5.1. The Reuters dataset

3.5.2. Preparing the data

3.5.3. Building our network

3.5.4. Validating our approach

3.5.5. Generating predictions on new data

3.5.6. A different way to handle the labels and the loss

3.5.7. On the importance of having sufficiently large intermediate layers

3.5.8. Further experiments