Predicting Stock Prices with Artificial Neural Networks
Continuing the same project of stock price prediction from the last chapter, in this chapter I will introduce and explain neural network models in depth. We will start by building the simplest neural network and go deeper by adding more layers to it. We will cover neural network building blocks and other important concepts, including activation functions, feedforward, and backpropagation. We will also implement neural networks from scratch with scikit-learn and TensorFlow. We will pay attention to how to learn with neural networks efficiently without overfitting, utilizing dropout and early stopping techniques. Finally, we will train a neural network to predict stock prices and see whether it can beat what we achieved with the three regression algorithms in the previous chapter.
We will cover the following topics in this chapter:
- Demystifying neural networks
- From shallow neural networks to deep learning ...
