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Hands-On Neural Networks with Keras

Hands-On Neural Networks with Keras

By : Purkait
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Hands-On Neural Networks with Keras

Hands-On Neural Networks with Keras

By: Purkait

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)
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1
Section 1: Fundamentals of Neural Networks
5
Section 2: Advanced Neural Network Architectures
10
Section 3: Hybrid Model Architecture
13
Section 4: Road Ahead

Signal Processing - Data Analysis with Neural Networks

Having acquired substantial knowledge on neural networks, we are now ready to perform our first operation using them. We will start with processing signals, and see how a neural network is fed data. You will be mesmerized at how increasing the levels and complexity of neurons can actually make a problem look simple. We will then look at how language can be processed. We will make several predictions using datasets.

In this chapter, we will cover the following topics:

  • Processing signals
  • Images as numbers
  • Feeding a neural network
  • Examples of tensors
  • Building a model
  • Compiling the model
  • Implementing weight regularization in Keras
  • Weight regularization experiments
  • Implementing dropout regularization in Keras
  • Language processing
  • The internet movie reviews dataset
  • Plotting a single training instance
  • One-hot encoding
  • Vectorizing...
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