Desktop Tkinter app to train and test a Keras CNN image classifier from folders of images. Set image size, epochs, and test split, watch a live accuracy plot, preview predictions, and save or load .keras models.
Study fork of eriklindernoren/Keras-GAN: Keras implementations of GAN variants, kept as reference material for CS42 deep learning sessions.
Benchmark KNN, Decision Tree, Random Forest, MLP, XGBoost, and a CNN on the Olivetti Faces dataset, comparing accuracy against training time. Notebook with side by side plots and an ipywidgets demo UI.
Background Tracks: a customizable ambient sound mixer built with Python and Tkinter. Mix and loop rain, birds, stream, campfire, and more for focus, relaxation, or sleep. Windows .exe in Releases.
End to end traffic sign image classifier built with Keras CNNs. Includes Jupyter and Colab notebooks, a local Python script with train / predict / GUI modes, and a pretrained model.
Explainable AI demo: apply Grad-CAM to a CNN trained on the TensorFlow Flowers dataset to visualize which image regions drive each prediction. Jupyter notebook with sample outputs.
Tiny two feature CSV dataset labelled Red / Green / Blue, made for first classification exercises (KNN, Decision Tree) in CS42 workshops.
Run a Google Teachable Machine image model locally in Python with Keras and OpenCV. Fixes the common version mismatch when loading exported keras_model.h5 files. Script and notebook included.
First OpenCV lab: open the webcam, draw a target on the center pixel, and print its BGR value on a keypress. A short hands on intro to real time video with Python.
Beginner Decision Tree classifier that predicts flower type from sepal and petal length with pandas and scikit-learn. CLI options for depth, test split, seed, and tree plot export.
Study fork of THU-MIG/yolov10 (real time end to end object detection), used as reference material for CS42 computer vision workshops.
Seven ways numbers fool people. Short posts, real news cases, pandas notebooks and animated figures.
Lab activity that trains a small Keras CNN on your own image folders with OpenCV preprocessing, then classifies new images through a Tkinter GUI.
Grade 10 classroom activity (Unit 9): a beginner Decision Tree that classifies flower type from sepal and petal length with scikit-learn. Includes the original activity pack and CLI options for depth, seed, and tree plots.
Classroom Decision Tree activity that predicts whether a small business location will succeed from foot traffic, area income, and nearby competitors. Built in sample dataset, CLI options, and tree plot export.
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