Greetings to you, fellow sentient being; welcome to our exciting journey. The journey itself is to understand the concepts and inner workings behind an elusively powerful computing paradigm: the artificial neural network (ANN). While this notion has been around for almost half a century, the ideas accredited to its birth (such as what an agent is, or how an agent may learn from its surroundings), date back to Aristotelian times, and perhaps even to the dawn of civilization itself. Unfortunately, people in the time of Aristotle were not blessed with the ubiquity of big data, or the speeds of Graphical Processing Unit (GPU)-accelerated and massively parallelized computing, which today open up some very promising avenues for us. We now live in an era where the majority of our species has access to the building blocks and tools required to assemble artificially...
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Table Of Contents
Hands-On Neural Networks with Keras
By :
Hands-On Neural Networks with Keras
By:
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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