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Intermediate AI Prompting - Neural Networks
By Eric Centore
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About this ebook
This book provides an introduction to neural networks, a type of artificial intelligence used in many applications. The book is divided into five sections.
The first section introduces neural networks and explains the different types. It covers what a neural network is, how it works, and the different types of neural networks.
The second section covers how to build a neural network. It explains the preprocessing of data, choosing a model, and training the network.
The third section covers the applications of neural networks. It covers image recognition, natural language processing, and autonomous vehicles.
The fourth section covers the challenges of neural networks. It covers overfitting, data availability, and interpretability.
The fifth section provides a summary of the book and looks at future directions. It covers the main points of the book and provides suggestions for future research.
This book provides an introduction to neural networks and covers the different types, how to build a neural network, the applications of neural networks, and the challenges of neural networks. It also provides a summary of the book and looks at future directions.
The first section introduces neural networks and explains the different types. It covers what a neural network is, how it works, and the different types of neural networks.
The second section covers how to build a neural network. It explains the preprocessing of data, choosing a model, and training the network.
The third section covers the applications of neural networks. It covers image recognition, natural language processing, and autonomous vehicles.
The fourth section covers the challenges of neural networks. It covers overfitting, data availability, and interpretability.
The fifth section provides a summary of the book and looks at future directions. It covers the main points of the book and provides suggestions for future research.
This book provides an introduction to neural networks and covers the different types, how to build a neural network, the applications of neural networks, and the challenges of neural networks. It also provides a summary of the book and looks at future directions.
LanguageEnglish
PublisherLulu.com
Release dateJul 17, 2023
ISBN9781312326231
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Intermediate AI Prompting - Neural Networks - Eric Centore
Image 1
Intermediate AI Prompting - Neural Networks
by
Eric Centore
Copyright 2023
ISBN: 978-1-312-32623-1
Imprint: Lulu.com
Table of Contents
I. Introduction to Neural Networks 3
A. What is a Neural Network? 3
B. Types of Neural Networks 4
II. Building a Neural Network 5
A. Preprocessing Data 5
B. Choosing a Model 6
C. Training the Network 7
III. Applications of Neural Networks 8
A. Image Recognition 8
B. Natural Language Processing 9
C. Autonomous Vehicles 10
IV. Challenges of Neural Networks 11
A. Overfitting 11
B. Data Availability 12
C. Interpretability 13
V. Conclusion 15
A. Summary 15
B. Future Directions 15
References 16
I. Introduction to Neural Networks:
A. What is a Neural Network?
Neural networks are a type of artificial intelligence (AI) that is modeled after the human brain. They are composed of interconnected nodes, or neurons, that are designed to process information and learn from it. Neural networks are used in a variety of applications, from facial recognition to self-driving cars. In this article, we will explore what a neural network is, how it works, and some of the potential applications of this technology.
A neural network is a type of machine learning algorithm that is modeled after the human brain. It is composed of interconnected nodes, or neurons, that are designed to process information and learn from it. Each neuron is connected to other neurons in the network, and these connections are weighted based on the strength of the connection. The neurons are then activated based on the input they receive, and the output is determined by the weighted connections.
Neural networks are used in a variety of applications, from facial recognition to self-driving cars. They are used to identify patterns in data, classify objects, and make predictions. For
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