Advanced-Machine-Learning

An Advanced Course on Machine Learning (mainly Deep Learning), Tailored Toward Engineers

Welcome! This course provides a deeper education in Machine Learning and it's use across various engineering domains. Download the notebooks, open them in Google Colab, and code along as we cover topics ranging from convolutions and computer vision; to recurrent neural networks and other time series methods; to advanced topics and model deployment!

Outline

Here is a quick (running) description of what each notebook covers:

• 01-understanding-convolutions.ipynb: An overview of what convolutions are, how to implement them in PyTorch, and a visualization exercise for several popular methods.
• 02-model-training-with-pytorch.ipynb: A tutorial of neural network training in PyTorch.
• 03-recurrent-neural-networks.ipynb: An implementation of a basic Recurrent Neural Network from scratch in PyTorch.
• 04-temporal-convolutional-networks.ipynb: An implementation of a basic Temporal Convolutional Network from scratch in PyTorch.
• 05-transformers.ipynb: An implementation of a simple transformer, with encoder block only, for a regression task.

Contributions

Feel free to submit a pull request for any issues or improvements!
Author: Megan Chiovaro, PhD (@mchiovaro)