TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials
The Everything Drawer of Deep Learning Notebooks
A curated stockpile of notebooks and links spanning deep learning frameworks, data tools, and industry verticals.
Not currently ranked — collecting fresh signals.
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What it does
This repository collects and organizes IPython notebooks, external tutorials, and short examples across deep learning and classical machine learning. It acts as an index to resources for PyTorch, TensorFlow, Keras, scikit-learn, pandas, and assorted other tools, with sections that occasionally wander into probabilistic programming and cloud tooling. The maintainer pitches it as a broad educational resource aimed at industry areas like healthcare and transportation, though most of the current material focuses on foundational framework mechanics.
The interesting bit
The repo functions like a time-capsule of the deep-learning tooling explosion: Theano tutorials, basic TensorFlow exercises, PyTorch autograd introductions, and even a D-language MNIST example via Netflix’s VectorFlow all share the same table of contents. That breadth makes it either a useful historical index or a cluttered attic, depending on your patience.
Key highlights
- Sections for PyTorch, TensorFlow, Keras, UBER Pyro, scikit-learn, pandas, matplotlib, and AWS tooling
- Includes linked probabilistic programming examples (Pyro) and a VectorFlow/D-language MNIST reference
- Aggregates external notebook collections, including PyTorch official tutorials and community TensorFlow guides
- Claims active expansion into GPU programming, Data-Centric AI, and Web3 intersections
- Organized by topic with nbviewer links for immediate browser viewing
Caveats
- Several index entries—notably Torch/Lua and MXNET—have empty links, suggesting incomplete curation
- Much of the value is in aggregation: many notebooks are hosted or authored elsewhere
- The README promises daily 2023–2024 updates and emerging Web3 coverage, while the visible corpus still centers on older framework introductions like Theano and basic TensorFlow exercises
Verdict
Useful as a broad, high-level map if you are exploring the ML tooling landscape and want one bookmark to rule them all. Avoid if you are looking for a coherent course or original, maintained library code.
Frequently asked
- What is TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials?
- A curated stockpile of notebooks and links spanning deep learning frameworks, data tools, and industry verticals.
- Is Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials open source?
- Yes — TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials is an open-source project tracked on heatdrop.
- What language is Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials written in?
- TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials is primarily written in Python.
- How popular is Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials?
- TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials has 4k stars on GitHub.
- Where can I find Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials?
- TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials is on GitHub at https://github.com/TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials.