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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.
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Tracking mentions began in Dec 2020.
5 ways to keep your skills fresh after finishing a coding bootcamp
5 projects | dev.to | 28 Nov 2021
One way to improve your projects and coding skills is to try new models and libraries. For example, if you did classification with logistic regression, try also with random forest; if you used Tensorflow, now try Keras; if you scraped a website with BeautifulSoup, now do it with Scrapy. You get the point.
[P] Walkthrough of Keras.Model Internals. Includes: distribution, performance optimizations, callbacks, training loop, and more.
2 projects | reddit.com/r/MachineLearning | 23 Nov 2021
The source for the keras.Model class has grown to be several thousand lines of code. This makes it incredibly challenging to sift through, especially for beginners.
Data Science toolset summary from 2021
13 projects | dev.to | 13 Nov 2021
Keras - Keras is an open-source software library that provides a Python interface for artificial neural networks. Keras acts as an interface for the TensorFlow library. Link - https://keras.io/
structuring larger projects, and good practises
2 projects | reddit.com/r/learnpython | 21 Oct 2021
1 project | reddit.com/r/tensorflow | 18 Oct 2021
I think I found my answer here Thank you for your help
7 projects | news.ycombinator.com | 9 Sep 2021
Top 10 Python Libraries for Machine Learning
14 projects | dev.to | 9 Sep 2021
Website: https://keras.io/ Github Repository: https://github.com/keras-team/keras Developed By: various Developers, initially by Francois Chollet Primary purpose: Focused on Neural Networks
[D] Getting Started
1 project | reddit.com/r/SubSimulatorGPT2 | 7 Sep 2021
I also recommend trying to understand the software that's being built by the machine learning class. If you want to build your own machine learning software, check out Keras (http://keras.io/) and the machine learning API's that Keras provides.
JAX - COMPARING WITH THE BIG ONES
2 projects | reddit.com/r/CryptocurrencyICO | 6 Sep 2021
These four points lead to an enormous differentiation in the ecosystem: Keras, for example, was originally thought to be almost completely focused on point (4), leaving the other tasks to a backend engine. In 2015, on the other hand, Autograd focused on the first two points, allowing users to write code using only "classic" Python and NumPy constructs, providing subsequently many options for point (2). Autograd's simplicity greatly influenced the development of the libraries to follow, but it was penalized by the clear lack of the points (3) and (4), i.e. adequate techniques to speed up the code and sufficiently abstract modules for neural network development.
What are the icons used in the banner of this subreddit?
4 projects | reddit.com/r/learnmachinelearning | 5 Sep 2021
What are some alternatives?
scikit-learn - scikit-learn: machine learning in Python
MLP Classifier - A handwritten multilayer perceptron classifer using numpy.
tensorflow - An Open Source Machine Learning Framework for Everyone
TFLearn - Deep learning library featuring a higher-level API for TensorFlow.
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
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
skflow - Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning
gensim - Topic Modelling for Humans
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.
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.