资源说明：Giuseppe Ciaburro; Balaji Venkateswaran 著 Neural Network and Artificial Intelligence Concepts Introduction Inspiration for neural networks How do neural networks work? Layered approach Weights and biases Training neural networks Supervised learning Unsupervised learning Epoch Activation functions Different activation functions Linear function Unit step activation function Sigmoid Hyperbolic tangent Rectified Linear Unit 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() Let us go through the code line-by-line Implementation using nnet() library Let us go through the code line-by-line Deep learning Pros and cons of neural networks Pros Cons Best practices in neural network implementations Quick note on GPU processing Summary 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 Confusion matrix True Positive Rate True Negative Rate Accuracy Precision and recall F-score Receiver Operating Characteristic curve Learning in neural networks Back to backpropagation Neural network learning algorithm optimization Supervised learning in neural networks Boston dataset Neural network regression with the Boston dataset Unsupervised learning in neural networks? Competitive learning Kohonen SOM Summary Deep Learning Using Multilayer Neural Networks Introduction of DNNs R for DNNs Multilayer neural networks with neuralnet Training and modeling a DNN using H2O Deep autoencoders using H2O Summary 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 implementation using RSNNS Summary Training and Visualizing a Neural Network in R Data fitting with neural network Exploratory analysis Neural network model Classifing breast cancer with a neural network Exploratory analysis Neural network model The network training phase Testing the 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 Summary Recurrent and Convolutional Neural Networks Recurrent Neural Network The rnn package in R LSTM model Convolutional Neural Networks Step #1 – filtering Step #2 – pooling Step #3 – ReLU for normalization Step #4 – voting and classification in the fully connected layer Common CNN architecture - LeNet Humidity forecast using RNN Summary Use Cases of Neural Networks – Advanced Topics TensorFlow integration with R Keras integration with R MNIST HWR using R LSTM using the iris dataset Working with autoencoders PCA using H2O Autoencoders using H2O Breast cancer detection using darch Summary

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