For tasks such as these, we need deep networks with multiple hidden layers if we are hoping to learn any representative features for our output classes. We also need a nice dataset to practice our understanding and familiarize ourselves with the tools we will be using to design our intelligent systems. Hence, we come to our first hands-on neural network task as we introduce ourselves to the concepts of computer vision, image processing, and hierarchical representation learning. Our task at hand is to teach computers to read numbers not as 0 and 1s, as they already do, but more in the manner of how we would read digits that are composed by our own kin. We are speaking of handwritten digits, and for this task, we will be using the iconic MNIST dataset, the true hello world of deep learning datasets. For our first example, there are good theoretical and practical...
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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