So far, we have seen how we can train a simple feedforward neural network on Keras for an image classification task. We also saw how we can mathematically represent image data as a high-dimensional geometric shape, namely a tensor. We saw that a higher-order tensor is simply composed of tensors of a smaller order. Pixels group up to represent an image, which in turn group up to represent an entire dataset. Essentially, whenever we want to employ the learning mechanism of neural networks, we have a way to represent our training data as a tensor. But what about language? How can we represent human thought, with all of its intricacies, as we do through language? You guessed it—we will use numbers once again. We will simply translate our texts, which are composed of sentences, which themselves are composed of words, into the universal language of mathematics...
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Book Overview & Buying
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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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