My workshop on machine learning using python language to implement different algorithms (University of Tabriz, Iran, 2017).
- Week 01 and 02: Introduction to Numpy and Matplotlib packages
- Week 03 and 04: Using Scikit Learn for Supervised Learning
- Week 05: Using Scikit Learn for Unsupervised Learning
- Week 06: Linear classification
- Week 07: Implementing Loss functions (Softmax loss and SVM loss)
- Week 08: Implementing gradient descent, Backpropagation and Artifitial Neural Networks (MLP)
- Week 09: Advanced topics including dropout, batch normalization, weight initialization and other optimization methods(Adam, RMSProp)
- Week 10: Inroduction to Deep Learning and implementing a Convolutional Neural Network (CNN) for image classification.
- A basic knowledge of Python programming language.
- A good understaning of Machine Learning.
- Linear Algebra
- containing anything you need to learn and of course to use machine learning in real world applications:
- http://wwww.snrazavi.ir/
Note: The materials of this workshop are inspired from awesome lectures presented by Andrej Karpathy at Stanford, 2016.
- Parts 6 to 8 are inspired from the wonderful course cs231n.
- Parts 5 and 6 are heavily inspired from SciPy 2016 Scikit-learn Tutorial.