Many may claim that the hierarchically nested pattern recognition technique leveraged by CNNs very much resembles the functioning of our own visual cortex. This may be true at a certain level. However, the visual cortex implements a much more complex architecture and makes it run efficiently on about 10 watts of energy. Our visual cortex also does not easily get fooled by images where face-like features appear, (although this phenomenon occurs often enough to have secured its proper term in modern neuroscience. Pareidolia is a term associated with the human mind interpreting signals in a manner to generate higher-level concepts, where none actually exists. Scientists have shown how this phenomenon is related to the earlier activation of neurons located in the fusiform gyrus area of the visual cortex, responsible for several visual recognition and classification...
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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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