Then, we simply add a few more layers of convolution, batch normalization, and dropouts, progressively building our network until we reach the final layers. Just like in the MNIST example, we will leverage densely connected layers to implement the classification mechanism in our network. Before we can do this, we must flatten our input from the previous layer (16 x 16 x 32) to a 1D vector of dimension (8,192). We do this because dense layer-based classifiers prefer to receive 1D vectors, unlike the output from our previous layer. We proceed by adding two densely connected layers, the first one with 128 neurons (an arbitrary choice) and the second one with just one neuron, since we are dealing with a binary classification problem. If everything goes according to plan, this one neuron will be supported by its cabinet of neurons...
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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