Learning about the Feynman technique recently, I decided to practice it immediately by writing this post.
This is a technique that helps you to learn concepts quickly and thoroughly.
Based on your chosen topic, you apply the following steps to consolidate your understanding.
Step 1. Explain the concept in your own words in only simple words without looking at any reference materials.
Step 2. Review the parts that you find it difficult to explain
Step 3. Improve the explanation in step 1
Continue the above process until you are familiar with the chosen topic. Each time your brain process the…
To ensure that dependencies are installed before we run a notebook, it is common to see a bunch of
pip install ,
apt-get commands at the beginning.
DeepNote offers an elegant way to manage these installation scripts with the
init.ipynb, so we could keep our main notebook file clean and ensure that the dependencies are installed every time we spin up the virtual machine.
This works well when the number of packages are relatively small and when we have to experiment on different packages. However, there are some drawbacks.
It is a good practice to craft carefully the…
DeepNote is a new data science notebook platform that is still in beta. Its unique feature that improves collaboration shows great potential. To try out the platform, I decided to run a Minecraft server on it for fun.
For the code, I used MineColab, which does the same thing on Google Colaboratory. The code is modified so it fits the DeepNote environment.
This story will show you around DeepNote and get you started with the platform.
Before we start, please note that:
Notebooks are not designed to running servers like this. More importantly, it is not a good idea to…
While playing with the quest Create ML with BigQuery ML, I got stuck with the challenge lab Create ML Models with BigQuery ML: Challenge Lab because of the overly strict checkpoints that did not let me pass even I got the correct answer.
I am writing this to help anyone else that got stuck and would also like to share some of my thoughts at the end.
The challenge uses the Austin Bike dataset. It is better to have a look to get some basic idea about the data before you start working on it.