Thinking of starting with Deep Learning,
Machine Learning and Data Science?

Transformers have become the defacto standard for NLP tasks nowadays.

PyTorch has sort of became one of the de facto standards for creating Neural Networks now, and I love its interface.

Before I even begin this article, let me just say that I love iPython Notebooks, and Atom is not an alternative to Jupyter in any way.

Most of us in data science has seen a lot of AI-generated people in recent times, whether it be in papers, blogs, or videos.

With ML Engineer job roles in all the vogue and a lot of people preparing for them, I get asked a lot of times by my readers to recommend courses for the ML engineer roles particularly and not for the Data Science roles.

Pandas is one of the best data manipulation libraries in recent times.

As Alexander Pope said, to err is human. By that metric, who is more human than us data scientists?

Data Exploration is a key part of Data Science. And does it take long?

Just recently, I had written a simple tutorial on FastAPI, which was about simplifying and understanding how APIs work, and creating a simple API using the framework.

Creating my own workstation has been a dream for me if nothing else.