Python Machine Learning By Example - Fourth Edition

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Predicting Stock Prices with Artificial Neural Networks

Continuing the same project of stock price prediction from the last chapter, in this chapter, we will introduce and explain neural network models in depth. We will start by building the simplest neural network and go deeper by adding more computational units 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, TensorFlow, and PyTorch. 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 ...