Top 23 Jupyter Notebook Machine Learning Projects
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A linear regression model
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Standing recommendation for beginners (we should eventually make a wiki) is https://madewithml.com/
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OPS
OPS - Build and Run Open Source Unikernels. Quickly and easily build and deploy open source unikernels in tens of seconds. Deploy in any language to any cloud.
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three educational courses- Web Dev, ML, and IoT for beginners. Note re using educational resources as a strategy for marketing , at least the ML course links to various Azure services. Google does this a bunch as well, with Collab notebooks often being used to demo educational materials.
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Project mention: Good practices for neural network training: identify, save, and document best models | dev.to | 2022-01-04
If you are unaware of what fastai is, its official description is:
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Project mention: I'm failing to download a repository correctly | reddit.com/r/u_No_Possibility_7588 | 2022-01-18
# Install steps - download the `ml-agents` repository `git clone https://github.com/Unity-Technologies/ml-agents` - create a Python folder in `ml-agents` and clone `social_rl` repo into it `svn export https://github.com/google-research/google-research/trunk/social_rl` - copy `environments.py` and `gymwrappers.py` into this Python folder - create a python3.8 environment and install `social_rl` requirements `conda create -n mlagents python=3.8` `pip install -r requirements.txt` - install `ml-agents_envs`, `ml-agents` and `gym-unity` from the `ml-agents` repository `python install setup.py`
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homemade-machine-learning
🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
Project mention: Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained | reddit.com/r/MachineLearning | 2021-10-27 -
Project mention: [Q] What's the community's opinion of "interpretable ML/AI"? | reddit.com/r/statistics | 2022-01-14
I've become a zealot about parametric stats, specifically from the Bayesian paradigm. Something about studying the core business problem, choosing the best distribution(s), and making inferences has been really rewarding for me. But increasingly, I'm seeing tools like SHAP, which allegedly enable users of black-box ML models to intuit what/how their models "think". (SHAP is just one example.)
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SonarLint
Deliver Cleaner and Safer Code - Right in Your IDE of Choice!. SonarLint is a free and open source IDE extension that identifies and catches bugs and vulnerabilities as you code, directly in the IDE. Install from your favorite IDE marketplace today.
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I’m a fan of fast book by fastai.
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python-machine-learning-book
The "Python Machine Learning (1st edition)" book code repository and info resource
Project mention: What is the purpose of meshgrid in Python / NumPy? | reddit.com/r/codehunter | 2022-01-06I am studying "Python Machine Learning" from Sebastian Raschka, and he is using it for plotting the decision borders. See input 11 here.
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The majority of this code is taken from the TensorFlow-Tutorials. I highly recommend them to those who want to get started with TensorFlow.
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2 packages I want to mention is pandas-profiling and Mito.
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amazon-sagemaker-examples
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Amazon SageMaker Asynchronous Inference with Hugging Face Model Amazon SageMaker Asynchronous Inference is a new capability in SageMaker that queues incoming requests and processes them asynchronously. SageMaker currently offers two inference options for customers to deploy machine learning models: 1) a real-time option for low-latency workloads 2) Batch transform, an offline option to process inference requests on batches of data available upfront. Real-time inference is suited for workloads with payload sizes of less than 6 MB and require inference requests to be processed within 60 seconds. Batch transform is suitable for offline inference on batches of data. This notebook provides an introduction on how to use the SageMaker Asynchronous inference capability with Hugging Face models. This notebook will cover the steps required to create an Asynchronous inference endpoint and test it with some sample requests.
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Project mention: Engineering Trade-Offs in Automatic Differentiation: from TensorFlow and PyTorch to Jax and Julia - Stochastic Lifestyle | reddit.com/r/programming | 2021-12-26
Apple really is focusing on CoreML rather than differentiable swift, that was more of the vision of Swift4TF, which really was driven mostly by Google, until it was cancelled (I assume because of Chris Latner leaving google for SiFive): https://github.com/tensorflow/swift
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H2O
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
Built and founded various web3 projects for last 2 years such as OpenArt and 8RealmDojo for last 2 years as well as being high performing student in CTU in Prague and SeoulTech. Was offered internships in Amazon and H2O.ai. Created robots assistants using robots from SoftBank.
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cleverhans
An adversarial example library for constructing attacks, building defenses, and benchmarking both
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Project mention: Machine Learning for Trading: Notebooks, resources and references accompanying the book Machine Learning for Algorithmic Trading. Courses - star count:5136.0 | reddit.com/r/algoprojects | 2022-01-21
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Project mention: Anyone willing to help me out on a discord call or something? | reddit.com/r/learnmachinelearning | 2021-05-27
This is the model I’m trying to understand and create my own version of: https://github.com/tensorflow/docs/blob/master/site/en/r1/tutorials/sequences/recurrent_quickdraw.md
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Project mention: [D] Open source projects for interpretability | reddit.com/r/MachineLearning | 2021-04-28
You should check out Captum for PyTorch: https://captum.ai/ and tf-explain or lucid (this one is the framework used by distill) for Tensorflow although I think they are both oriented towards Vision interpretability (not sure if you are looking for that).
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Project mention: [P] ray-skorch - distributed PyTorch on Ray with sklearn API | reddit.com/r/MachineLearning | 2022-01-04
I'm the principal author of ray-skorch, a library that lets you run distributed PyTorch training on large-scale datasets while providing a familiar, scikit-learn compatible skorch API, integrating well with the rest of the scikit-learn ecosystem.
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t81_558_deep_learning
Washington University (in St. Louis) Course T81-558: Applications of Deep Neural Networks
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A while back I open sourced a repository implementing fundamental machine learning algorithms in Python, along with the most important theoretical information. I originally created the repository for myself when preparing for AI residency interviews. You can find the original Reddit post here.
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Project mention: What is Probabilistic Programming? | reddit.com/r/learnmachinelearning | 2021-09-06
This tutorial explains what is probabilistic programming & provides a review of 5 frameworks (PPLs) using an example taken from Chapter 4 of Statistical Rethinking by Dr. Richard McElreath. Frameworks (PPLs) reviewed are - Stan (https://mc-stan.org/) PyMC3 (https://docs.pymc.io/) Tensorflow Probability (https://www.tensorflow.org/probability) Pyro/NumPyro (https://pyro.ai/) Turing.jl (https://turing.ml/stable/) I also provide the basic review of a great library called arviz (https://arviz-devs.github.io/arviz/), which can be used for all the above-mentioned PPLs to do Exploratory Data Analysis of Bayesian Models. Here is the link to the notebook in which I have implemented the example model using the above Frameworks/PPLs https://colab.research.google.com/drive/1zgR2b0j2waGi1ppnIe1rw7emkbBXtMqF?usp=sharing
Jupyter Notebook Machine Learning related posts
- Machine Learning for Trading: Notebooks, resources and references accompanying the book Machine Learning for Algorithmic Trading. Courses - star count:5136.0
- Machine Learning for Trading: Notebooks, resources and references accompanying the book Machine Learning for Algorithmic Trading. Courses - star count:5136.0
- Machine Learning for Trading: Notebooks, resources and references accompanying the book Machine Learning for Algorithmic Trading. Courses - star count:5136.0
- Machine Learning for Trading: Notebooks, resources and references accompanying the book Machine Learning for Algorithmic Trading. Courses - star count:5136.0
- Anyone can suggest a very noob friendly ML trading algorithm for a beginner like me?
- [Tutorial] Building recommendation systems with Tensorflow and ScaNN (Scalable Nearest Neighbors)
- AI Poem: Fire and Ice by Robert Frost
Index
What are some of the best open-source Machine Learning projects in Jupyter Notebook? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | TensorFlow-Examples | 41,601 |
| 2 | MadeWithML | 29,409 |
| 3 | ML-For-Beginners | 28,735 |
| 4 | fastai | 21,844 |
| 5 | google-research | 21,522 |
| 6 | homemade-machine-learning | 18,774 |
| 7 | shap | 15,176 |
| 8 | fastbook | 14,148 |
| 9 | python-machine-learning-book | 11,460 |
| 10 | TensorFlow-Tutorials | 8,941 |
| 11 | pandas-profiling | 8,415 |
| 12 | amazon-sagemaker-examples | 6,433 |
| 13 | swift | 6,038 |
| 14 | H2O | 5,691 |
| 15 | cleverhans | 5,381 |
| 16 | machine-learning-for-trading | 5,147 |
| 17 | docs | 5,045 |
| 18 | pycaret | 4,943 |
| 19 | lucid | 4,360 |
| 20 | skorch | 4,302 |
| 21 | t81_558_deep_learning | 4,232 |
| 22 | machine_learning_basics | 3,594 |
| 23 | probability | 3,571 |
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