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Top 23 Python machine-learning-algorithm Projects
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Project mention: Building an AI Game Bot 🤖Using Imitation Learning and 3D Convolution ResNet | dev.to | 2024-01-02
def compute_mean_std(dataloader): ''' We assume that the images of the dataloader have the same height and width source: https://github.com/aladdinpersson/Machine-Learning-Collection/blob/master/ML/Pytorch/Basics/pytorch_std_mean.py ''' # var[X] = E[X**2] - E[X]**2 channels_sum, channels_sqrd_sum, num_batches = 0, 0, 0 for batch_images, labels in tqdm(dataloader): # (B,H,W,C) batch_images = batch_images.permute(0,3,4,2,1) channels_sum += torch.mean(batch_images, dim=[0, 1, 2, 3]) channels_sqrd_sum += torch.mean(batch_images ** 2, dim=[0, 1, 2,3]) num_batches += 1 mean = channels_sum / num_batches std = (channels_sqrd_sum / num_batches - mean ** 2) ** 0.5 return mean, std compute_mean_std(dataloader)
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CodeRabbit
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Project mention: Show HN: Pathway – Build Mission Critical ETL and RAG in Python (NATO, F1 Used) | news.ycombinator.com | 2024-06-13
The main factor impacting the RAM requirement of the instance is the size of the data that you feed into it, especially if you need an in-memory index. (If you are curious about peak memory use etc., you can profile Pathway memory use in Grafana: https://github.com/pathwaycom/pathway/tree/main/examples/pro....)
One point to clarify is that "Pathway Community" is self-hosted, and the "8GB RAM - 4 cores" value is just a limit on the dimension of your own/cloud machine that the framework will effectively use. Currently, if you would like to get a "free" cloud machine to go with your project, we suggest going for "Pathway Scale" and reaching out through the #Developer Assist link - add a mention that you are interested in cloud credits. You can also go with 3rd party hosting providers like http://render.com/ who have a (somewhat modest) free tier for Docker instances, or reasonably priced ones like fly.io https://fly.io/docs/about/pricing/.
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igel
a delightful machine learning tool that allows you to train, test, and use models without writing code
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machine_learning_refined
Notes, Python demos / notebooks, and free chapters for the 2nd edition of the university textbook "Machine Learning Refined".
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scikit-learn-intelex
Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application
There is a bit similar project which supports Intel GPU offloading: https://github.com/intel/scikit-learn-intelex
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Options-Trading-Strategies-in-Python
Developing Options Trading Strategies using Technical Indicators and Quantitative Methods
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awesome-monte-carlo-tree-search-papers
A curated list of Monte Carlo tree search papers with implementations.
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pretty-print-confusion-matrix
Confusion Matrix in Python: plot a pretty confusion matrix (like Matlab) in python using seaborn and matplotlib
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Machine-Learning
Implementation of different ML Algorithms from scratch, written in Python 3.x (by Gautam-J)
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Time-series-classification-and-clustering-with-Reservoir-Computing
Library for implementing reservoir computing models (echo state networks) for multivariate time series classification and clustering.
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zoofs
zoofs is a python library for performing feature selection using a variety of nature-inspired wrapper algorithms. The algorithms range from swarm-intelligence to physics-based to Evolutionary. It's easy to use , flexible and powerful tool to reduce your feature size.
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I'm a Master's student in Artificial Intelligence at UofT. Right now, I'm adding the "ML (Machine Learning) Streaming" feature to PyMilo. PyMilo is an open-source Python package that provides a transparent, safe, and end-to-end way for users to export pre-trained machine-learning models.
Transparency and non-executable export format is a serious thing, take a look at https://embracethered.com/blog/posts/2022/machine-learning-a....
After version 0.9 release, PyMilo became feature-completed with full support of scikit-learn models, now it's time to move on to PyTorch and then Tensorflow. But we decided to add the "ML Streaming" feature before getting into PyTorch, in order to provide an easy way to smoothly stream your ML model. By using the "ML Streaming" feature you can easily deploy your model into the remote server, connect to it from the client side, and choose the working mode, either delegation or local mode, through delegation mode your requests will be relayed to the remote server and you can easily work with your remote model from any devices without any further dependencies, and finally, you can download model for local use.
We will release the 1.0 (tenth) version of PyMilo around Sep 16th, this release will be the first release to have the "ML Streaming" feature with support of REST API, and we will next add other protocols such as Websocket.
Here is PyMilo: https://github.com/openscilab/pymilo
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crispy
Crispy is a machine-learning platform to make video-games montages efficiently. It uses a neural network to detect highlights in the video-game frames (by Flowtter)
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Project mention: Show HN: Krixik – Easily sequence small/specialized AI models (pip installable) | news.ycombinator.com | 2024-11-04
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osdg-tool
OSDG is an open-source tool that maps and connects activities to the UN Sustainable Development Goals (SDGs) by identifying SDG-relevant content in any text. The tool is available online at www.osdg.ai. API access available for research purposes.
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Python machine-learning-algorithms discussion
Python machine-learning-algorithms related posts
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Show HN: Krixik – Easily sequence small/specialized AI models (pip installable)
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[P] Request to Test PyMilo: A New Python Library for Machine Learning I/O
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Pointers to reduce false negatives while not sacrificing accuracy in deep learning
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I want to learn more about AI and Machine Learning
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[P] Desktop app. makes fuel consumption estimation with machine learning.
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My desktop app that makes fuel consumption estimation with artificial intelligence. Open source github repo. Please give a star for support me.
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[P] An application that optimizes the fuel consumption of your vehicles with artificial intelligence. Open source. Please star for support.
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Index
What are some of the best open-source machine-learning-algorithm projects in Python? This list will help you:
Project | Stars | |
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1 | Machine-Learning-Collection | 7,725 |
2 | pathway | 5,025 |
3 | igel | 3,083 |
4 | tslearn | 2,921 |
5 | modAL | 2,237 |
6 | machine_learning_refined | 1,716 |
7 | GIMP-ML | 1,434 |
8 | scikit-learn-intelex | 1,233 |
9 | Options-Trading-Strategies-in-Python | 838 |
10 | wtte-rnn | 767 |
11 | awesome-monte-carlo-tree-search-papers | 649 |
12 | pretty-print-confusion-matrix | 530 |
13 | pecos | 517 |
14 | Machine-Learning | 408 |
15 | Time-series-classification-and-clustering-with-Reservoir-Computing | 353 |
16 | Renate | 281 |
17 | zoofs | 245 |
18 | groupImg | 233 |
19 | pymilo | 149 |
20 | crispy | 97 |
21 | xplainable | 58 |
22 | krixik-docs | 38 |
23 | osdg-tool | 35 |