A reference folder mapping some of my deep-learning repositories: RNN, GAN, DCGAN, seq2seq, Transfer learning, Autoencoders, Semi-supervised learning
Notebook implementation of semi-supervised seismic interpretation deep-learning model (facies + channels + faults)
A comprehensive repository featuring implementations of supervised and semi-supervised machine learning models. This collection includes Python code, Jupyter notebooks (.ipynb), and thorough documentation for each model, making it an ideal resource for both learning and experimentation.
Machine-Learning-Practice is a curated collection of classic ML algorithms and projects, organized into intuitive Jupyter notebooks. With structured input/output directories, saved model files, and hands-on examples—from linear regression and logistic classification to clustering, ensembles, and tuning.
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