Is Deep-Learning-Experiments open source?

Yes — roatienza/Deep-Learning-Experiments is open source, released under the MIT license.

What language is Deep-Learning-Experiments written in?

roatienza/Deep-Learning-Experiments is primarily written in Jupyter Notebook.

How popular is Deep-Learning-Experiments?

roatienza/Deep-Learning-Experiments has 1.2k stars on GitHub.

Where can I find Deep-Learning-Experiments?

roatienza/Deep-Learning-Experiments is on GitHub at https://github.com/roatienza/Deep-Learning-Experiments.

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roatienza/Deep-Learning-Experiments

A full deep-learning curriculum, free, with working notebooks

Lecture notes and runnable code covering MLPs to Mamba, for developers who want to close the gap between theory and implementation.

★1.2k stars Jupyter Notebook Learning ML Frameworks

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What it does This repo bundles lecture notes (PDFs) and Jupyter notebooks into a structured course on deep learning. Topics run from classic MLPs, CNNs, and RNNs up to modern additions like Mamba, SAM2, flow matching, diffusion models, and a from-scratch GPT-2 trained on TinyStories. A “Practice” section adds tooling coverage: PyTorch Lightning, ONNX export, TensorRT, Triton serving, and even a Docker guide.

The interesting bit Most tutorials pick a lane—math or code. This one pairs a PDF explainer with a runnable notebook for nearly every topic, so you can read the theory, then immediately break the implementation. The 2025 revision also keeps the content current; Mamba and flow matching sit alongside transformers and GANs rather than being scattered across blog posts.

Key highlights

Theory + code pairs for 15+ topics, from autoencoders to LLM agents
Includes newer architectures: Mamba, SAM2 segmentation, normalizing flows, diffusion/flow matching
LLM section covers training GPT-2 from scratch, fine-tuning, and validation on TinyStories
Practice modules on deployment: ONNX, TensorRT, PyTriton, Docker
Citable academic-style notes with a BibTeX entry provided

Caveats

Video links are listed but uniformly blank ("-") in the current README
Some topics (Agents, Detection, Optimization, Regularization) have notes but no code yet
“HuggingFcae” typo suggests light proofreading; external PDFs on Google Drive could drift or break

Verdict Ideal for self-taught developers or students who want a coherent, free curriculum with working PyTorch examples. Skip it if you need production-hardened libraries or video-first learning; the value here is the structured breadth, not depth on any single frontier model.

Frequently asked

What is roatienza/Deep-Learning-Experiments?: Lecture notes and runnable code covering MLPs to Mamba, for developers who want to close the gap between theory and implementation.
Is Deep-Learning-Experiments open source?: Yes — roatienza/Deep-Learning-Experiments is open source, released under the MIT license.
What language is Deep-Learning-Experiments written in?: roatienza/Deep-Learning-Experiments is primarily written in Jupyter Notebook.
How popular is Deep-Learning-Experiments?: roatienza/Deep-Learning-Experiments has 1.2k stars on GitHub.
Where can I find Deep-Learning-Experiments?: roatienza/Deep-Learning-Experiments is on GitHub at https://github.com/roatienza/Deep-Learning-Experiments.