roatienza/Deep-Learning-Experiments resource

Health v2 · maintenance only

Adoption not part of the score

1198 stars · 768 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of deep learning lecture notes (PDFs), videos, and PyTorch Jupyter notebook experiments covering topics from MLPs and CNNs to Transformers, Mamba, and segmentation. It serves as a hands-on educational resource for understanding deep learning theory and practice.

Use cases

• learn deep learning from scratch with pytorch notebooks
• understand how transformers work with code examples
• study mamba and state space models
• find lecture notes on cnns and rnns
• hands-on experiments for variational autoencoders
• learn image segmentation with SAM2
• supplement a university deep learning course

When to choose

• you want theory notes paired with runnable PyTorch notebooks
• you are a student or self-learner covering classic and modern DL architectures
• you want minimal, educational implementations like Mamba on MNIST

When to avoid

• you need production-ready model code or a maintained library
• you want a polished course platform with graded exercises
• you need large-scale training pipelines or MLOps tooling

Facets

learning-resource · maturity active

deep-learning machine-learning nlp computer-vision speech-recognition deep-learning machine-learning artificial-intelligence tutorials python cross-platform jupyter-notebooks pytorch lecture-notes transformers mamba cnn rnn autoencoder self-study

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