Welcome to our biweekly selection of Free and Open machine learning news. Created using our own opinionated selection and summary algorithm. FOSS machine learning is crucial for everyone. Machine Learning is a complex technology. So keep it simple.
1 Introducing spaCy v2.3
For most languages, spaCy v2.3 introduces custom word vectors trained using spaCy’s language-specific tokenizers on data from OSCAR and Wikipedia. We’ll start making prereleases on spacy-nightly soon, so stay tuned. New languages spaCy v2.3 provides new model families for five languages : Chinese, Danish, Japanese, Polish and Romanian. If you’re using your own custom models, you’ll need to retrain them with the new version.
Using sudachipy greatly simplifies installing spaCy for Japanese, which is now possible with a single command: pip install spacy[ja].
2 PyCaret: A new low-code machine learning library that makes your ML live easier?
PyCaret is a new ML framework. And since it is FOSS and has some potentials you can find it in the FOSS-ML Guide. PyCaret is a promising concept. So attractive that I could not resit to play a bit with this product. You can find my observations in this simple overview.
(PyCaret)
3 Housing Market Prediction Problem using Different Machine Learning Algorithms: A Case Study
Developing an accurate prediction model for housing prices is always needed for socio-economic development and well-being of citizens. In this paper, a diverse set of machine learning algorithms such as XGBoost, CatBoost, Random Forest, Lasso, Voting Regressor, and others, are being employed to predict the housing prices using public available datasets.
4 How to Learn Machine Learning and Deep Learning: A Guide for Software Engineers
Let’s face it, most people have no time for that or the patience. In our the FOSS ML Guide you find all crucial learning resources. Open and Free. But this article is nice for starters and has some tips for learning resources too.
(Link)
5 Machine Learning Reddit Advanced Courses List
I love collecting open learning resources for machine learning, deep learning, NLP and related subjects. This list is a great collection of mostly open quality material from universities. Most things are for more experienced people.
6 Get More Out of Your Annotated Medical Images with Self-Supervised Learning
How can practitioners in the field of medical imaging best use DL given limited data?
7 A Review of Popular Deep Learning Architectures
A deep into some models. Big advancements were made in just two years, leading to boosts in accuracy and performance.
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8 TensorFlow, Keras and deep learning, without a PhD
A great hands-on tutorial (123 minutes). Created by Google. Yes you will use the Google Colab notebooks. And not the (FOSS) Jupypter Lab which is of course more privacy friendly.
(Link)
The FOSS Machine Learning News Blog is a brief overview of open machine learning news from all over the world. Free and Open machine learning means that everyone must be able to develop, test and play and deploy machine learning solutions. Read and share the FOSS ML Guide! And remember:You are invited to join the Free and Open Machine Learning open collaboration project.