A Python, data analysis, neural network, and computer vision workshop for students at Kutztown Area High School
You can find resources for learning the syntax and basics of python in intro_python/syntax/intro_python.ipynb.
Several demos can be found in intro_python/demos showing how Python is used in science for data analysis. We have a Jupyter notebook on performing text analysis in intro_python/demos/text_analysis/moby_dick.ipynb. Jordan provides some of his simulation code from his Physics senior thesis at Tufts University in intro_python/demos/simulations, along with a demonstration for using Python to work with Excel files in intro_python/demos/excel_demo.
Most of this work is based on Connely Barnes' course at the University of Virginia, CS 4501 - Introduction to Computer Vision.
We have a Jupyter notebook explaining basic image processing with filters to make edge and face detectors in cv_filters/computer_vision_filters.ipynb.
We have a Jupyter notebook explaining how to train and use a neural network in Keras to classify handwritten digits from the MNIST dataset, found in digits/digit_classifier.ipynb.
We have a Jupyter notebook explaining how to use a Convolutional Neural Network to classify cat and dog images in cats_or_dogs/cat_or_dog.ipynb.
- 3Blue1Brown, the YouTube channel, has produced an excellent video series explaining neural networks ituitively. He also goes into some of the math behind why these neural networks work. Part 1, Part 2, Part 3, Part 3.a.
- Welch Labs, another YouTube channel, produced a 15 part series on computer vision and machine learning called Learning to See: Playlist
Neural Networks
Computer Vision
pip install jupyter keras tensorflow opencv-python scikit-image