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 (16 chapters)
Advanced Deep Learning with Keras
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Principles of VAEs

In a generative model, we're often interested in approximating the true distribution of our inputs using neural networks:

(Equation 8.1.1)

In the preceding equation,

are the parameters determined during training. For example, in the context of the celebrity faces dataset, this is equivalent to finding a distribution that can draw faces. Similarly, in the MNIST dataset, this distribution can generate recognizable handwritten digits.

In machine learning, to perform a certain level of inference, we're interested in finding

, a joint distribution between inputs, x, and the latent variables, z. The latent variables are not part of the dataset but instead encode certain properties observable from inputs. In the context of celebrity faces, these might be facial expressions, hairstyles, hair color, gender, and so on. In the MNIST dataset, the latent variables may represent the digit and writing styles.

is practically a distribution of input data points and their attributes...