Experiment with hidden layers and hidden units

The most commonly used layers in neural networks are fully-connected layers. In fully-connected layers, the units in two successive layers are all  connected. However, the units within a layer don't share any connections. As stated before, the connections between the layers are also called trainable parameters. The weights of these connections are trained by the network. The more connections, the more parameters and the more complex patterns can be modeled. Most state-of-the-art models have 100+ million parameters. However, a deep neural network with many layers and units takes more time to train. Also, with extremely deep models the time to infer predictions takes significantly longer (which can be problematic in a real-time environment). In the following chapters, we will introduce other popular layer types that are specific to their network types. 

Picking the correct number of hidden layers and hidden units can be important. When using too...