Hands-On Neural Networks with Keras: Design and create neural networks using deep learning and artificial intelligence principles

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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

Key benefits

Design and create neural network architectures on different domains using Keras

Integrate neural network models in your applications using this highly practical guide

Get ready for the future of neural networks through transfer learning and predicting multi network models

Description

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.

What you will learn

Understand the fundamental nature and workflow of predictive data modeling

Explore how different types of visual and linguistic signals are processed by neural networks

Dive into the mathematical and statistical ideas behind how networks learn from data

Design and implement various neural networks such as CNNs, LSTMs, and GANs

Use different architectures to tackle cognitive tasks and embed intelligence in systems

Learn how to generate synthetic data and use augmentation strategies to improve your models

Stay on top of the latest academic and commercial developments in the field of AI

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