Book Image

Advanced Deep Learning with Keras

By : Rowel Atienza
Book Image

Advanced Deep Learning with Keras

By: Rowel Atienza

Overview of this book

Recent developments in deep learning, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Deep Reinforcement Learning (DRL) are creating impressive AI results in our news headlines - such as AlphaGo Zero beating world chess champions, and generative AI that can create art paintings that sell for over $400k because they are so human-like. Advanced Deep Learning with Keras is a comprehensive guide to the advanced deep learning techniques available today, so you can create your own cutting-edge AI. Using Keras as an open-source deep learning library, you'll find hands-on projects throughout that show you how to create more effective AI with the latest techniques. The journey begins with an overview of MLPs, CNNs, and RNNs, which are the building blocks for the more advanced techniques in the book. You’ll learn how to implement deep learning models with Keras and TensorFlow 1.x, and move forwards to advanced techniques, as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You then learn all about GANs, and how they can open new levels of AI performance. Next, you’ll get up to speed with how VAEs are implemented, and you’ll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans - a major stride forward for modern AI. To complete this set of advanced techniques, you'll learn how to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.
Table of Contents (13 chapters)

Denoising autoencoder (DAE)

We're now going to build an autoencoder with a practical application. Firstly, let's paint a picture and imagine that the MNIST digits images were corrupted by noise, thus making it harder for humans to read. We're able to build a Denoising Autoencoder (DAE) to remove the noise from these images. Figure 3.3.1 shows us three sets of MNIST digits. The top rows of each set (for example, MNIST digits 7, 2, 1, 9, 0, 6, 3, 4, 9) are the original images. The middle rows show the inputs to DAE, which are the original images corrupted by noise. The last rows show the outputs of DAE:

Figure 3.3.1: Original MNIST digits (top rows), corrupted original images (middle rows) and denoised images (last rows)

Figure 3.3.2: The input to the denoising autoencoder is the corrupted image. The output is the clean or denoised image. The latent vector is assumed to be 16-dim.

As shown in Figure 3.3.2, the denoising autoencoder has practically the same structure as the autoencoder for MNIST...