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Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
The problem with open source: not enough contributors
2 projects | dev.to | 18 Mar 2023
In their report they show the 10 projects with the biggest number of contributors. The first one is microsoft/vscode with 19.8K contributors in 2022 and the 10th place is tensorflow/tensorflow with 4.4K contributors. That's really nice, but my guess is that most repositories have very few contributors.
I plan to build my own AI powered search engine for my portfolio. Do you know ones that are open-source?
6 projects | reddit.com/r/opensource | 17 Mar 2023
TensorFlow - This one needs no introduction. It’s widely-used and it has several tools and community resources for training and deploying ML/DL models. This one is JS based and I’m not too familiar with JS except that I know a lot of people use it for web development. What I like about it is that it’s used for speech and image recognition. And one of my favorites are text summarization.
OpenXLA Is Available Now
5 projects | news.ycombinator.com | 9 Mar 2023
If you mean StableHLO, then it has an MLIR dialect: https://github.com/openxla/stablehlo/blob/main/stablehlo/dia....
In the StableHLO spec, we are talking about this in more abstract terms - "StableHLO opset" - to be able to unambiguously reason about the semantics of StableHLO programs. However, in practice the StableHLO dialect is the primary implementation of the opset at the moment.
I wrote "primary implementation" because e.g. there is also ongoing work on adding StableHLO support to the TFLite flatbuffer schema: https://github.com/tensorflow/tensorflow/blob/master/tensorf.... Having an abstract notion of the StableHLO opset enables us to have a source of truth that all the implementations correspond to.
General instruccions to use pwnagotchi in others sbc orange pi 3 for example
4 projects | reddit.com/r/pwnagotchi | 1 Mar 2023
Install necessary dependencies: sudo apt-get update sudo apt-get upgrade sudo apt-get install build-essential git libhdf5-dev python-dev python-pip python-numpy python-wheel python-mock python-mockito python-pytest python-six python-h5py sudo pip install --upgrade pip sudo pip install setuptools Install Bazel: sudo apt-get install pkg-config zip g++ zlib1g-dev unzip python wget https://github.com/bazelbuild/bazel/releases/download/0.26.1/bazel-0.26.1-installer-linux-x86_64.sh chmod +x bazel-0.26.1-installer-linux-x86_64.sh ./bazel-0.26.1-installer-linux-x86_64.sh --user Clone TensorFlow 1.x: git clone https://github.com/tensorflow/tensorflow.git cd tensorflow git checkout r1.15 Configure TensorFlow: ./configure Build TensorFlow: bazel build --config=opt --local_resources=1024,1.0,1.0 --verbose_failures //tensorflow/tools/pip_package:build_pip_package bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg sudo pip install /tmp/tensorflow_pkg/tensorflow-1.15.0-cp27-cp27mu-linux_armv7l.whl Set up swap memory: sudo fallocate -l 4G /swapfile sudo chmod 600 /swapfile sudo mkswap /swapfile sudo swapon /swapfile Test TensorFlow: python -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
Want to use some weekend time to develop some useful open source tools- what would be useful?
4 projects | reddit.com/r/StableDiffusion | 26 Feb 2023
Heck, I don't know how involved you feel like getting, but providing Python 3.11 support for tensorflow would be super awesome.
Inafara de jocuri dezvoltate in Unreal Engine, ce tipuri de proiecte utilizeaza C++ in ziua de azi?
2 projects | reddit.com/r/programare | 24 Feb 2023
Uite ce scrie la limbaj aici: https://github.com/tensorflow/tensorflow
Google Python Style Guide
6 projects | news.ycombinator.com | 11 Feb 2023
I am pretty sure it used to be 2 spaces as well. Some public repositories such as https://github.com/tensorflow/tensorflow/tree/master/tensorf... appear to use 2 space indent throughout.
I think all these will be covered by the "be consistent" clause, and whoever made the first commit decides the style.
Creating Image Frames from Videos for Deep Learning Models
2 projects | dev.to | 7 Feb 2023
Converting the images to a tensor: Deep learning models work with tensors, so the images should be converted to tensors. This can be done using the to_tensor function from the PyTorch library or convert_to_tensor from the Tensorflow library.
How worried are you about AI taking over music?
13 projects | reddit.com/r/WeAreTheMusicMakers | 3 Feb 2023
Tensorflow 238k contributors
Python's "Disappointing" Superpowers
2 projects | reddit.com/r/programming | 2 Feb 2023
C++ is actually used in machine learning. More than 60% of TensorFlow code is in C++: https://github.com/tensorflow/tensorflow. With high level configs and prototyping is done in python.
We are the developers behind pandas, currently preparing for the 2.0 release :) AMA
9 projects | reddit.com/r/Python | 1 Mar 2023
There's an issue here about that https://github.com/scikit-learn/scikit-learn/discussions/25450
Machine learning with Julia - Solve Titanic competition on Kaggle and deploy trained AI model as a web service
13 projects | dev.to | 17 Feb 2023
This is not a book, but only an article. That is why it can't cover everything and assumes that you already have some base knowledge to get the most from reading it. It is essential that you are familiar with Python machine learning and understand how to train machine learning models using Numpy, Pandas, SciKit-Learn and Matplotlib Python libraries. Also, I assume that you are familiar with machine learning theory: types of machine learning problems like regression and classification, the concept and process of Supervised machine learning (fit/predict and evaluate quality using metrics) and common models used for it, including Random Forest Classifier, and it's implementation in SciKit-Learn Python library. Additionally, it would be great if you previously participated in Kaggle competitions, because to understand and run all code of this article you need to have an account on https://kaggle.com.
Best Websites For Coders
51 projects | dev.to | 25 Jan 2023
Scikit-learn : A Python module for machine learning build on top of SciPy
scikit-learn VS Rath - a user suggested alternative
2 projects | 12 Jan 2023
Boston Dataset was Removed from scikit-learn 1.2
2 projects | news.ycombinator.com | 30 Dec 2022
Can you really call this "banning the dataset"? https://github.com/scikit-learn/scikit-learn/commit/8a86e219...2 projects | news.ycombinator.com | 30 Dec 2022
2 projects | reddit.com/r/u_Matusadona_Wild303 | 25 Oct 2022
Machine Learning Pipelines with Spark: Introductory Guide (Part 1)
5 projects | dev.to | 23 Oct 2022
The concepts are similar to the Scikit-learn project. They follow Spark’s “ease of use” characteristic giving you one more reason for adoption. You will learn more about these main concepts in this guide.
How do you programmers make sense of production-level code?
2 projects | reddit.com/r/learnprogramming | 20 Oct 2022
If you look at the README for scikit-learn on GitHub, they say this.2 projects | reddit.com/r/learnprogramming | 20 Oct 2022
Take a smaller segment to look at. Opening up the front page to a Github repo can be quite daunting. https://github.com/scikit-learn/scikit-learn
What are some alternatives?
PaddlePaddle - PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Keras - Deep Learning for humans
Surprise - A Python scikit for building and analyzing recommender systems
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
LightGBM - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
MLflow - Open source platform for the machine learning lifecycle
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration