Hi there!

Let me start by wishing you all a very happy new year. The first month of a year tends to bring in some fresh vibes to renew our resolutions which couldn’t have been fulfilled in the year gone by. I have also updated my To-do list, and there are some great add ons that I wish to include in this newsletter in the coming months.

As for this edition, there are tutorials, paper summaries, and interviews with book authors. I have also shared some nice resources for machine learning enthusiasts. 

Decision trees can be visualized in multiple ways. For instance, the indentation nodes, the node-link diagram, or the icicle plots. While these techniques are helpful, they do not scale well, especially when the size of data increases. In such situations, not only does it become difficult to visualize the data, but interpreting and understanding the tree is also a challenge. 

BaobabView is a library created to overcome such problems, and in this article, I‘ll introduce you to its python implementation called pybaobabdt in detail, along with examples.

GeoPandas is a popular library used to analyze and work with geospatial data in Python. The library has added methods and utilities to support interactive visualizations in one of its recent updates. This is an excellent add-on to an already helpful library.

This is the second interview in the series of interviewing authors. In the last edition, I interviewed Alexey Grigorev, author of the book- Machine Learning Bookcamp. This time I had the privilege to interview Radek Osmulski

The Fastai community is well known for giving the world not only means to get into machine learning but also great researchers from time to time. Radek Osmulski is one such fastai-taught AI Research Engineer. He worked for several startups from Silicon Valley, Australia, and Dubai. In 2018 he won a Kaggle competition sponsored by Google. What may come as a surprise, Radek doesn’t have a formal background in math or computer science. There were many things about learning machine learning and becoming employable that he had to figure out. He documented all his learnings in his book →Meta-Learning: How To Learn Deep Learning And Thrive In The Digital World. In this interview, you’ll learn more about his idea behind the book, motivation, and advice for data science enthusiasts and writers. 

The Google Colab team shared a video highlighting some Colab features you may have missed.

MiniTorch is a DIY teaching library for machine learning engineers who wish to learn about the internal concepts underlying deep learning systems. It is a pure Python re-implementation of the Torch API designed to be simple, easy-to-read, tested, and incremental. The final library can run Torch code. The project was developed for the course ‘Machine Learning Engineering’ at Cornell Tech.

That is all for this edition. See you with another roundup next month. You can subscribe to receive the newsletter directly in your mailbox every month or share it with someone who could find them helpful.

Until next month,

Parul