We can see that each feature in our observation seems to be on a different scale. Some values range in the hundreds, while others are between 1 and 12, or even binary. While neural networks may still ingest unscaled features, it almost exclusively prefers to deal with features on the same scale. In practice, a network can learn from heterogeneously scaled features, but it may take much longer to do so without any guarantee of finding an ideal minimum on the loss landscape. To allow our network to learn in an improved way for this dataset, we must homogenize our data through the process of feature-wise normalization. We can achieve this by subtracting the feature-specific mean and dividing it by the feature-specific standard deviation for each feature in our dataset. Note that in live-deployed models (for the stock exchange, for example), such a scaling...
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Table Of Contents
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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