Breaking the Jargons – August Roundup
Welcome to my newsletter. In these monthly roundups, I’ll be sharing a quick recap of the articles that went live this month. Also, I’ll be highlight some of the things that I have enjoyed reading over the past month.
Here are the articles published in August:
- Advanced plots in Matplotlib — Part 1 & Part 2– A series of articles containing some advanced plots in Matplotlib.
- Getting Datasets for Data Analysis tasks — Advanced Google Search – A quick article on smartly using Google search to find datasets.
- Getting Datasets for Data Analysis tasks - Useful sites for finding datasets – This articles focusses on five useful data aggregator sites which hosts free and openly available datasets which can be used for the data analysis tasks.
- Getting ‘More’ out of your Kaggle Notebooks – This article is a compilation of my learnings when it comes to writing effective notebooks consisting of some do’s and dont’s.
WiMLDS-Hyderabad(Hyderabad women in Machine Learning & Data Science) organised their August Meetup on 14th August. The speaker for the event was Danielle Deterring , a Content Developer at NVIDIA’s Deep Learning Institute, where she teaches engineers the skills needed to further their careers in Data Science and AI. She shared her story of switching careers and shared some crucial points on the various nuances of the Data Science journey.
The Kaggle team has recently come out with a lot of add ons to the existing notebooks. I really like the new “Search StackOverflow” button which shows up under an error, in a notebook.
Kaggle’s latest course addresses the most common data cleaning problems so you can analyze your data faster.
- New Competition launch
Contradictory, My Dear Watson – Given a pair of sentences (a premise and a hypothesis), you need to predict whether they are related.
Lyft 2020 Motion Prediction for Self-Driving Cars– Build motion prediction models for self-driving vehicles
Resources of the Month
An interactive Free deep learning book with code, math, and discussions it provides NumPy/MXNet, PyTorch, and TensorFlow implementations.
Interactive learning tools that can help you understand different machine learning algorithms, in an intuitive manner.
Things to look out for
Upcoming Webinar on Teaching Machine learning for Social Good on 2nd September.
That is all for August. See you with another roundup , next month. You can signup below to receive similar newsletters in your mailbox, every month.
Until next time,