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Top 23 Jupyter Notebook neural-network Projects
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AppSignal
Monitoring that respects your time & budget. APM, error tracking, and dashboards for modern web apps. Ten-minute setup, transparent flat pricing, and support from engineers who actually use the product.
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python-machine-learning-book
The "Python Machine Learning (1st edition)" book code repository and info resource
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Machine-Learning-Specialization-Coursera
Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
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t81_558_deep_learning
T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis
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deep-learning-v2-pytorch
Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101
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alpha-zero-general
A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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coursera-deep-learning-specialization
Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models
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LSTM-Human-Activity-Recognition
Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier
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On the compute side there's the issue of scalability. CPUs are designed to perform a handful of operations at one time. They typically have a small number of dedicated integer, float, and other ALU configurations. Having dedicated matrix multiplication instructions would still lock that to how many matrix-capable ALUs there are in the CPU.
GPUs are designed to process a large number of calculations at once (so they can process triangles in 3D graphics). This makes them good at ML applications as they can process many of the matrix calculations at once. A 4090 has 16,384 CUDA cores (general compute ALUs) and 512 Tensor cores (dedicated matrix compute ALUs); a 5090 has 21,760 CUDA and 680 Tensor cores.
The other issue when training models (and running larger models) is the amount of VRAM (or RAM for CPUs) available. GPUs are limited in this aspect, whereas CPUs can have a lot higher memory. This affects things like batch size and the size of model that can be trained or fine-tuned.
https://unsloth.ai has guides for how to fine-tune existing models like Qwen 3.8 27B, memory requirements, etc.
https://medium.com/@kailaspsudheer/the-transformers-arithmet... has some information on training a base model. A 7B llama model is estimated at taking ~34GB memory for inference at F32, but was observed requiring 96GB memory when training (for the model weights, gradients, activations, and optimizer states).
Note: you can reduce the memory required for training by recomputing the gradients, at a cost of performance/time. You can also do other tricks like performing a QLoRA/LoRA pass on the model then merging that into the model to create a checkpoint.
I don't know what sized model you could train on 64GB/128GB RAM via a CPU.
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DeepLearningForNLPInPytorch
An IPython Notebook tutorial on deep learning for natural language processing, including structure prediction.
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Deep_Learning_Machine_Learning_Stock
Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders.
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torchdyn
A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods
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transformers-interpret
Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
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ILearnDeepLearning.py
This repository contains small projects related to Neural Networks and Deep Learning in general. Subjects are closely linekd with articles I publish on Medium. I encourage you both to read as well as to check how the code works in the action.
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Deep-Learning-In-Production
Build, train, deploy, scale and maintain deep learning models. Understand ML infrastructure and MLOps using hands-on examples.
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time-series-forecasting-pytorch
Acquiring data from Alpha Vantage and predicting stock prices with PyTorch's LSTM
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Jupyter Notebook neural-network discussion
Jupyter Notebook neural-network related posts
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Transformer from scratch HTTPS://github.com/Eamon2009/Transformer-language-model
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Neural Graffiti – Liquid Memory Layer for LLMs
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Understanding Automatic Differentiation in 30 lines of Python
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Like Diffusion but Faster: The Paella Model for Fast Image Generation
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FLaNK Stack for 15 May 2023
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GitHub - Deci-AI/super-gradients: Easily train or fine-tune SOTA co...GitHub - Deci-AI/super-gradients: Easily train or fine-tune SOTA co...
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Meet YOLO-NAS: An Open-Sourced YOLO-based Architecture Redefining State-of-the-Art in Object Detection
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A note from our sponsor - AppSignal
www.appsignal.com | 6 Oct 2026
Index
What are some of the best open-source neural-network projects in Jupyter Notebook? This list will help you:
| # | Project | Stars |
|---|---|---|
| 1 | handson-ml | 25,604 |
| 2 | python-machine-learning-book | 12,656 |
| 3 | TensorFlow-Tutorials | 9,263 |
| 4 | Machine-Learning-Specialization-Coursera | 7,992 |
| 5 | t81_558_deep_learning | 5,733 |
| 6 | deep-learning-v2-pytorch | 5,520 |
| 7 | alpha-zero-general | 4,518 |
| 8 | monodepth2 | 4,502 |
| 9 | machine_learning_basics | 4,415 |
| 10 | coursera-deep-learning-specialization | 4,382 |
| 11 | Andrew-NG-Notes | 3,763 |
| 12 | LSTM-Human-Activity-Recognition | 3,488 |
| 13 | yolov3-tf2 | 2,513 |
| 14 | hyperlearn | 2,493 |
| 15 | DeepLearningForNLPInPytorch | 1,955 |
| 16 | Deep_Learning_Machine_Learning_Stock | 1,785 |
| 17 | torchdyn | 1,576 |
| 18 | transformers-interpret | 1,415 |
| 19 | ILearnDeepLearning.py | 1,409 |
| 20 | Deep-Learning-In-Production | 1,263 |
| 21 | glasses | 447 |
| 22 | mango | 419 |
| 23 | time-series-forecasting-pytorch | 328 |
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