Uncover the power of artificial neural networks by implementing them through R code.
About This Book
- Develop a strong background in neural networks with R, to implement them in your applications
- Build smart systems using the power of deep learning
- Real-world case studies to illustrate the power of neural network models
Who This Book Is For
This book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need!
What You Will Learn
- Set up R packages for neural networks and deep learning
- Understand the core concepts of artificial neural networks
- Understand neurons, perceptrons, bias, weights, and activation functions
- Implement supervised and unsupervised machine learning in R for neural networks
- Predict and classify data automatically using neural networks
- Evaluate and fine-tune the models you build.
Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning.
This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases.
By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book.
Style and approach
A step-by-step guide filled with real-world practical examples.
Table of contents
Neural Network and Artificial Intelligence Concepts
- Inspiration for neural networks
- How do neural networks work?
- Layered approach
- Weights and biases
- Training neural networks
- Activation functions
- Different activation functions
- Which activation functions to use?
- Perceptron and multilayer architectures
- Forward and backpropagation
- Step-by-step illustration of a neuralnet and an activation function
- Feed-forward and feedback networks
- Gradient descent
- Taxonomy of neural networks
- Simple example using R neural net library - neuralnet()
- Implementation using nnet() library
- Deep learning
- Pros and cons of neural networks
- Best practices in neural network implementations
- Quick note on GPU processing
Learning Process in Neural Networks
- What is machine learning?
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Training and testing the model
- The data cycle
- Evaluation metrics
- Learning in neural networks
- Back to backpropagation
- Neural network learning algorithm optimization
- Supervised learning in neural networks
- Unsupervised learning in neural networks
- Deep Learning Using Multilayer Neural Networks
- Perceptron Neural Network Modeling – Basic Models
Training and Visualizing a Neural Network in R
- Data fitting with neural network
- Classifing breast cancer with a neural network
- Early stopping in neural network training
- Avoiding overfitting in the model
- Generalization of neural networks
- Scaling of data in neural network models
- Ensemble predictions using neural networks
- Recurrent and Convolutional Neural Networks
- Use Cases of Neural Networks – Advanced Topics
- Title: Neural Networks with R
- Release date: September 2017
- Publisher(s): Packt Publishing
- ISBN: 9781788397872
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