Teaching Networks to Generate Digits
In the previous chapter, we covered the building blocks of neural network models. In this chapter, our first project will recreate one of the most groundbreaking models in the history of deep learning, Deep Belief Network (DBN). DBN was one of the first multi-layer networks for which a feasible learning algorithm was developed. Besides being of historical interest, this model is connected to the topic of this book because the learning algorithm makes use of a generative model in order to pre-train the neural network weights into a reasonable configuration prior to backpropagation.
In this chapter, we will cover:
- How to load the Modified National Institute of Standards and Technology (MNIST) dataset and transform it using TensorFlow 2's Dataset API.
- How a Restricted Boltzmann Machine (RBM) – a simple neural network – is trained by minimizing an "energy" equation that resembles formulas from physics...
