ML notebooks
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Updated
Jan 20, 2022 - Jupyter Notebook
ML notebooks
A curated collection of hands-on notebooks exploring core machine and deep learning concepts. Each notebook focuses on a specific topic - from linear models and foundational elements like broadcasting and autograd to advanced tasks such as custom layers, transfer learning, sequence modeling with RNNs, and representation learning with autoencoders.
Implementation of a PLA algorithm using Jupyter Notebooks.
This repository delves into the role of activation functions in perceptron-based classification models. It features a comprehensive Jupyter notebook demonstrating different activation functions, their mathematical foundations, and their impact on model performance.
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