rasbt/deeplearning-models
A collection of deep learning architecture implementations (MLPs, CNNs, RNNs, transformers) in PyTorch and TensorFlow as executable notebooks.
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This repository contains Jupyter notebooks implementing various deep learning models and architectures from scratch using PyTorch and TensorFlow. It covers traditional ML algorithms (perceptron, logistic regression), multilayer perceptrons with regularization, convolutional neural networks, recurrent networks, and modern architectures. Both framework implementations are provided side-by-side for comparison.
Frequently asked
- What is rasbt/deeplearning-models?
- A collection of deep learning architecture implementations (MLPs, CNNs, RNNs, transformers) in PyTorch and TensorFlow as executable notebooks.
- Is deeplearning-models open source?
- Yes — rasbt/deeplearning-models is open source, released under the MIT license.
- What language is deeplearning-models written in?
- rasbt/deeplearning-models is primarily written in Jupyter Notebook.
- How popular is deeplearning-models?
- rasbt/deeplearning-models has 17.5k stars on GitHub.
- Where can I find deeplearning-models?
- rasbt/deeplearning-models is on GitHub at https://github.com/rasbt/deeplearning-models.