The GRU can be considered the younger sibling of the LSTM, which we will look at Chapter 6, Long-Short Term Memory Networks. In essence, both leverage similar concepts to modeling long-term dependencies, such as remembering whether the subject of the sentence is plural, when generating following sequences. Soon, we will see how memory cells and flow gates can be used to address the vanishing gradient problem, while better modeling long term dependencies in sequence data. The underlying difference between GRUs and LSTMs is in the computational complexity they represent. Simply put, LSTMs are more complex architectures that, while computationally expensive and time-consuming to train, perform very well at breaking down the training data into meaningful and generalizable representations. GRUs, on the other hand, while computationally less intensive, are limited in their representational...
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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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