Since we have a two-dimensional latent space, we can simply plot out the representations as a 2D manifold where encoded instances of each digit class may be visualized with respect to their proximity to other instances. This allows us to inspect the continuous latent space that we spoke of before and see how the network relates to different features in the 10-digit classes (0 to 9) to each other. To do this, we revisit the encoding module from our VAE, which can now be used to produce a compressed latent space from some given data. Thus, we use the encoder module to make predictions on the test set, thereby encoding these images the latent space. Finally, we can use a scatterplot from Matplotlib to plot out the latent representation. Do note that each individual point represents an encoded instance from the test set. The colors denote the different...
Hands-On Neural Networks with Keras
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Hands-On Neural Networks with Keras
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Overview of this book
Neural networks are used to solve a wide range of problems in different areas of AI and deep learning.
Hands-On Neural Networks with Keras will start with teaching you about the core concepts of neural networks. You will delve into combining different neural network models and work with real-world use cases, including computer vision, natural language understanding, synthetic data generation, and many more. Moving on, you will become well versed with convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, autoencoders, and generative adversarial networks (GANs) using real-world training datasets. We will examine how to use CNNs for image recognition, how to use reinforcement learning agents, and many more. We will dive into the specific architectures of various networks and then implement each of them in a hands-on manner using industry-grade frameworks.
By the end of this book, you will be highly familiar with all prominent deep learning models and frameworks, and the options you have when applying deep learning to real-world scenarios and embedding artificial intelligence as the core fabric of your organization.
Table of Contents (16 chapters)
Preface
Overview of Neural Networks
A Deeper Dive into Neural Networks
Signal Processing - Data Analysis with Neural Networks
Section 2: Advanced Neural Network Architectures
Convolutional Neural Networks
Recurrent Neural Networks
Long Short-Term Memory Networks
Reinforcement Learning with Deep Q-Networks
Section 3: Hybrid Model Architecture
Autoencoders
Generative Networks
Section 4: Road Ahead
Contemplating Present and Future Developments
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