|about 2 months ago||4 days ago|
|MIT License||GNU General Public License v3.0 or later|
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.
2 projects | reddit.com/r/backtickbot | 26 Sep 2021
I'm using Omniboard (https://github.com/vivekratnavel/omniboard) with Sacred (https://github.com/IDSIA/sacred) for tracking experiments. You can specify custom Observers in Sacred so the model metrics and logs will be saved to a local directory or to a remote DB (e.g., MongoDB). I use a MongoDB database hosted on Atlas. Unlike other suggested options, Sacred and Omniboard are free. Atlas free tier comes with 512MB of free storage which is a huge amount if you're uploading only log files to it.
[D] Facebook Visdom vs Google Tensorboard for Pytorch
5 projects | reddit.com/r/MachineLearning | 26 Sep 2021
I'm using Omniboard (https://github.com/vivekratnavel/omniboard) with Sacred (https://github.com/IDSIA/sacred) for tracking experiments. You can specify custom Observers in Sacred so the model metrics and logs will be saved to a local directory or to a remote DB (e.g., MongoDB). I use a MongoDB database hosted on Atlas. Unlike other suggested options, Sacred and Omniboard are free. Atlas free tier comes with 512MB of free storage which is a huge amount if you're uploading only log files to it. ex = Experiment() ex.observers.append(FileStorageObserver(EXPERIMENTS_ROOT)) ex.observers.append(MongoObserver(url=MONGODB_URL, db_name='sacred'))
Can someone tell me good libraries you use on a day to day basis that increases your research productivity in ML/AI?
1 project | reddit.com/r/MLQuestions | 24 May 2021
sacred helped me log my experiments. I did setup my environment only once 4 years ago, and since then I have a list of all my training runs with the hyperparameters and results.
[D] How to be more productive while doing Deep Learning experiments?
10 projects | reddit.com/r/MachineLearning | 25 Feb 2021
For 1, setup an experiment tracking framework. I found Sacred to be helpful https://github.com/IDSIA/sacred.
That time I optimized a Python program by 5000x
5 projects | reddit.com/r/Python | 11 Jan 2022
The report output for scalene does look much nicer, but the slowness for me dropped me from continuing to use it. Maybe there's some bad interaction with tensorflow/pytest. I can try to make an example, but I'd guess if you try running it on tensorflows actual unit tests (something like this) you'd get similar behavior.
5% of 666 Python repos had comma typo bugs (inc V8, TensorFlow and PyTorch)
20 projects | news.ycombinator.com | 7 Jan 2022
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.
What are some alternatives?
scikit-learn - scikit-learn: machine learning in Python
MLP Classifier - A handwritten multilayer perceptron classifer using numpy.
MLflow - Open source platform for the machine learning lifecycle
tensorflow - An Open Source Machine Learning Framework for Everyone
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
TFLearn - Deep learning library featuring a higher-level API for TensorFlow.
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.