1. Why Machine Learning is exciting

1. What is Machine Learning?

1. Logistic Regression

1. Interpretation of Logistic Regression

1. Motivation for Multilayer Perceptron

1. Multilayer Perceptron Concepts

1. Multilayer Perceptron Math Model

1. Deep Learning

1. Example: Document Analysis

1. Interpretation of Multilayer Perceptron

1. Transfer Learning

1. Model Selection

1. Early History of Neural Networks

1. Hierarchical Structure of Images

1. Convolutional Filters

1. Convolutional Neural Networks

1. CNN Math Model

1. How the Model learns

1. Advantages of Hierachical Features

1. CNN on Real Images

1. Applications and Use in Practice

1. Deep Learning and Transfer Learning

Done!

Course session

Welcome and Introduction round

Introduction of the course, opencampus, the course instructor and the course participants

Tool Set-Up

• Colab

• Editor (VSCode)

• Virtual Environments

• Git/Github

Walk-through

PyTorch 101 (Lab 01)

A visual overview of the workflow in the Colab notebook you can get in the PyTorch diagram below:

To-do

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1. Watch the following introduction video to the PyTorch framework

1. Watch all the videos on the next page - they are derived from a former Coursera Course

2. Go for your own through the Colab Notebook above (Pytorch101) and try to understand and repeat the steps for your own. Thereby you should also solve Task 1-3 in the notebook. You can create therefore a copy of the notebook in your Drive or download the notebook to work locally on it. Ensure that you sufficient computing resources available (i.e gpu) if you choose to work locally.

😊😊

Try to improve the accuracy in the PyTorch 101 notebook by tweaking the amount of layers and number of neurons

😊😊😊

Familiarize yourself with basic PyTorch Tutorials:

• (First part)