Book Image

Hands-On Neural Networks with Keras

By : Niloy Purkait
Book Image

Hands-On Neural Networks with Keras

By: Niloy 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)
Free Chapter
Section 1: Fundamentals of Neural Networks
Section 2: Advanced Neural Network Architectures
Section 3: Hybrid Model Architecture
Section 4: Road Ahead

Section 1: Fundamentals of Neural Networks

This section familiarizes the reader with the basics of operating neural networks, how to select appropriate data, normalize features, and execute a data processing pipeline from scratch. Readers will learn how to pair ideal hyperparameters with appropriate activation, loss functions, and optimizers. Once completed, readers will have experienced working with real-world data to architect and test deep learning models on the most prominent frameworks.

This section comprises the following chapters:

  • Chapter 1, Overview of Neural Networks
  • Chapter 2, Deeper Dive into Neural Networks
  • Chapter 3, Signal Processing – Data Analysis with Neural Networks