Since we know that the maximum number of unique words in our entire corpus is 12,000, we can assume that the longest possible review can only be 12,000 in length. Hence, we can make each review a vector of length 12,000, containing binary values. How does this work? Suppose we have a review of two words: bad and movie. A list containing these words in our dataset may look like [6, 49]. Instead, we can represent this same review as a 12,000-dimensional vector populated with 0s, except for the indices of 6 and 49, which would instead be 1s. What you're essentially doing is creating 12,000 dummy features to represent each review. Each of these dummy features represents the presence or absence of any of the 12,000 words in a given review. This approach is also known as one-hot encoding. It is commonly used to encode features and categorical labels alike in various...
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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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