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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

A Deeper Dive into Neural Networks

In this chapter, we will encounter more in-depth details of neural networks. We will start from building a perceptron. Moving on, we will learn about activation functions. And we will also be training our first perceptron.

In this chapter, we will cover the following topics:

  • From the biological to the artificial neuron – the perceptron
  • Building a perceptron
  • Learning through errors
  • Training a perceptron
  • Backpropagation
  • Scaling the perceptron
  • A single layered network

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