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My first experiments with Machine Learning (TensorFlow in Jupyter notebook).

License: MIT License

Jupyter Notebook 62.38% Batchfile 0.08% C 36.71% C++ 0.05% HTML 0.78%

machinelearning's Introduction

MachineLearning

My first experiments with Machine Learning (TensorFlow in Jupyter notebook).

Projects

My first project uses a Jupyter notebook and a list of a quarter of a million credit card transaction to make a neural network that detects the fraudulent ones.
My second project classifies 131 species of fruit (and vegetables and nuts). So here the I use a convolutional neural network that takes images as input.
My third project tries to predict the sin(x) from x, a network even simpler than for the credit card transactions. However, the real goal is to run the model on the ESP32, a goal that is reached. The same sketch even runs on ESP8266, albeit slower. There are two intermezzos. The first maps a piece-wise linear curve to a two-layer neural net. The second uses that technique to hand-craft a sine predictor.
The fourth project is no longer to educate me. This implements rock-paper-scissors on an ESP32 with a camera connected. A supporting project (ESP32CAM) captured the images that were use for training the neural network. A follow-up project (TFLcam) is the resulting end-product. It can be used as a sensor in a rock-paper-scissors Lego Mindstorms robot.