In the previous chapter, we marveled over the visual cortex and leveraged some insights from the way it processes visual signals to inform the architecture of Convolutional Neural Networks (CNNs), which form the base of many state-of-the-art computer vision systems. However, we do not understand the world around us with vision alone. Sound, for one, also plays a very important role. More specifically, we humans love to communicate and express intricate thoughts and ideas through sequences of symbolic reductions and abstract representations. Our built-in hardware allows us to interpret vocalizations or demarcations thereof, composing the base of human thought and collective understandings, upon which more complex representations (such as human languages, for instance) may be composed. In essence, these sequences of symbols are reduced representations of...
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Table Of Contents
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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