Introduction to Deep Learning

Explain the difference between artificial intelligence, machine learning, and deep learning. Recognize the power of deep learning by reviewing popular examples of deep learning applications

Use PyTorch to preprocess data and use maximum likelihood, cross-entropy, and probability to measure model performance. Apply gradient descent to minimize error. Implement a backpropagation algorithm and identify key components of perceptrons.

Explain essential concepts in neural networks and design neural network architectures. Distinguish between problems based on the objective of the model. Implement appropriate architectures for model objectives

Define a loss function and optimization method to train a neural network. Distinguish between overfitting and underfitting, and identify the causes of each. Optimize the training process using early stopping, regularication, dropout, learning rate decay, and momentum. Distinguish between batch and stochastic gradient descent and build a neural network with PyTorch and run data through it. Test and validate a trained network to ensure it generalizes.

Develop a handwritten digit recognition system in PyTorch. Then, use data preprocessing skills to load data appropriately for use in models. Develop a neural network using PyTorch and write a training loop that trains the model with the loaded data. Lastly, apply advanced training techniques to improve accuracy on the test set.