This is the repo includes materials for python crash course for Tripods NSF StemForAll 2021 REU program

Program website: https://people.math.rochester.edu/faculty/iosevich/stemforall2021.html

Write a function
Matrix operation
Working with data structures
Knowledge with general programming
Working with Jupyter Notebook

Running experiment and document in Jupyter Notebook

Reading and processing dataset

Implementation of Neural Network
Back-prop, Gradient descent.
Implementation of NN layer, feed forward.

VC-Dimension

Mutual-class classification

Python and Numpy tutorial: https://cs231n.github.io/python-numpy-tutorial/#jupyter-and-colab-notebooks

CNN: https://cs231n.github.io/convolutional-networks/

BackProp: https://cs231n.github.io/optimization-2/#backprop

Efficificient back-prop by Prof.Yann Lecun: http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf

Classify hand-written digit: http://neuralnetworksanddeeplearning.com/chap1.html

Cool visualization:
https://cs231n.github.io/assets/nn3/opt2.gif
https://cs231n.github.io/assets/nn3/opt1.gif

Goal: (Probability, JupNotebook, running experiments with python) Estimated Time: 2-3 hour

From python’s built-in ramdomInt (sampling a number from [0,1]) simulate and estimate the value PI

Draw a square with side length 1, sample a point, count how many points lies within the circle Visualize sampling process in Jupitor Notebook See how simulation converge to the real value of PI

Goal: (Image processing, matrix operation, convolution kernel on image, convoluting, pooling) Estimated Time: 3-4 hour

Given a image apply different matrix operation Turn image gray-scale Blurring the image
[1,1,1]
[1,1,1]
[1,1,1]
Sharpen the image

Goal: (ML, NN, data process, Gradient Descent,) Estimated Time: 5 hour VC-Dimension
Build a classifier on MINST-fashion dataset

Goal: (CNN, back prop, multi-class classification) Estimated Time: 1 week training time

Build a vanilla CNN model to perform multi-class classification)