In this chapter, we have been introduced to deep learning and the use of neural networks. An artificial neutral network consists of an input layer and an output layer, with one or more hidden layers in between. Each layer consists of artificial neurons, and each artificial neuron receives weighted inputs that are summed together with a bias. An activation function transforms these inputs into an output, and feeds it as input to another neuron.
Using the TensorFlow Python library, we built a deep learning model with four hidden layers to predict the prices of a security. The dataset is preprocessed by scaling and split into training and testing data. Designing an artificial neuron network involves two phases. The first phase is to assemble the graph, and the second phase is to train the model. A TensorFlow session object provides an execution environment, where training...
