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Chapter 1 : Neural Network and Artificial Intelligence Concepts
Introduction
Inspiration for neural networks
How do neural networks work?
Layered approach
Weights and biases
Training neural networks
Epoch
Activation functions
Different actication 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 Iearning
Pros and cons of neural networks
Beet practices in neural network implementations
Quick note on GUP processing
Summary

Chapter 2 : Learning Process in Neural Networks
What is machine learning?
Supervised learning
Unsupervised learning
Training and testing the model
The data cycle
Evaluation metrics
Learning in neural networks
Back to backpropagation
Neural network learning algoriothm optimization
Unsupervised learning in neural networks
Summary

Chapter 3 : Deep Learning Using Mulilayer Neural Networks
Introduction of DNNs
R for DNNs
Multilayer neural networks with nuralnet
Training and modeling a DNN using H20
Deep autoencoders using H20
Summary

Chapter 4 : Perceptron Neural Network Modeling - Basic Models
Perceptrons and their applications
Simple perceptron - a linear separable classifier
Linear separation
The perceptron function in R
Multi - Layer Perceptron
MLP R implemnentation using RSNNS
Summary

Chapter 5 : 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 ocerfitting in the model
Generalization of neural networks
Scaling of data in neural network models
Ensemble predictions using neural networks
Summary

Chapter 6 : Recurrent and Covolutional Neural Networks
Recurrent Neural Network
The rnn package in R
LSTM model
Convolutional Neural Networks
Common CNN architecture - LeNet
Humidity forecast using RNN
Summary

Chapter 7 : Use Cases of Neural Networks - Advanced Topice
TensorFlow integration with R
Keras integration with R
MNIST HWR using R
Working with autoencoders
PCA using H20
Autoencoders using H20
Breast cancer detection using darch
Summary