There may only be four fundamental forces in our universe, but they are all signals. By signal, we mean any kind of feature representations we may have of a real-world phenomenon. Our visual world, for example, is full of signals that indicate motion, color, and shapes. These are very dynamic signals, and it is a miracle that biology is able to process these stimuli so accurately, even if we do say so ourselves. Of course, in the grander scheme of things, realizing that nature has had hundreds of millions of years to perfect this recipe may humble us, if only a little. For now, we can admire the marvel that is the human visual cortex, which is equipped with 140 million densely interconnected neurons. In fact, an entire series of layers (V1 – V5) exist through which information propagates as we engage in progressively more complex image processing tasks...
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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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