I encounter few great resources to be share for this week in machine learning. Let’s take a look at them!
Geometric Deep Learning Course [webpage][lecture videos]
Geometric Deep Learning (DGL) course publishes great course materials including slides and video recording on the topic of Geometric Foundations of Deep Learning (which I shared some time ago). The course was delivered as part of African Master’s in Machine Intelligence (AMMI 2021). I will take time to go through all the lecture videos of this course.
BERTopic [github][blog]
BERTopic is a topic modeling technique that performs a density-based clustering on document representation (encoded using a transformer-based model). BERTopic utilizes class-based TF-IDF (c-TF-IDF) to get important words on each topic.
This library provides easy-to-use API to perform BERTopic, and visualize topics. In addition, it also supports various pre-trained models such as Sentence Transformer, Flair (allows you to use Huggingface pre-trained transformer models), Spacy, Gensim, and Universal Sentence Encoder (USE).
How to avoid machine learning pitfalls: a guide for academic researchers [paper]
This paper discusses common mistakes when using machine learning techniques, and how to avoid them. The paper is very suitable to anyone new in machine learning area. It covers pitfalls in every stage of machine learning development including data preparation, model development, evaluation to reporting. Here is the outline of the paper.
That’s all for this week. Stay safe and see you next week.
