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Getting started with Anaconda Notebooks
Course Overview and Learning Objectives
Course Materials
What is AI and ML?
Neural Networks and Deep Learning
Neural Network Walkthrough
What Is Generative AI?
Are the Robots Taking Over?
Exercise: Deep Learning Model
Simple Classifier
Converting Data to PyTorch
Building the Model
Splitting Train/Test Data
Training and Evaluating the Model
Exercise: Prediction
Declaring the Neural Network
Linear Algebra Review
Forward Propagation
Derivatives and Chain Rule Review
Backpropagation
Stochastic Gradient Descent
Exercise: Gradient Descent on Linear Regression
The MNIST Dataset
Multilayer Perceptron
Convolutional Neural Network
Dropout Regularization
Exercise: Convolutional Layer
Time Series and Sequences
Tensor Shapes and Preparing Data
Autoregressive Linear Model in PyTorch
Recurrent Neural Networks in PyTorch
Exercise: Preparing Data for RNNs
Case Study: Uber Tempe Accident
Case Study: Uber Tempe Accident (Continued)
Overfitting and Bias
Overfitting and Bias (Continued)
Explainability and Operating Domain
Explainability and Operating Domain (Continued)
P-Hacking
Human Efforts and Labeling Data
Exercise: Scope This Problem
Summary
Practice Quiz
End of course survey
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