What is GenAI? Generative AI (GenAI) empowers end-users to generate content, such as images and text, quickly and easily. Entrepreneurs are taking advantage of this technology to create a growing number of startups that utilize GenAI models for various aspects of content creation. In the coming year, we can expect to see a proliferation of […]
The next coding frontier- comparing about Julia, Go & Rust with Python
Currently, Python is the dominant programming language of data science and machine learning and is popular for more general scripting. It’s pretty awesome compared to its predecessors like C/C++, FORTRAN due to its ease of use, flexibility and readability. Python also has an active and robust library culture after over 30 years of existence. However, […]
Software Engineering as a Data Scientist
Many of us in Data Science come from math, biology, chemistry or engineering or other non-Computer Science backgrounds, which may mean that we don’t have much experience writing and maintaining large code bases. Recently, I found myself getting frustrated with the structure of some of my code and searching for a better way to structure […]
Best 2021 Resources for Learning about AI/ML
For upskilling on AI/ML, I prefer taking a top-down approach i.e. starting with high level concepts then proceeding to more foundational topics (read: delve more into the theory) . I liked taking the breadth-first approach (rather than a depth-first approach) to initially understand AI/ML. Once I had a solid foundation, I easily pivoted to learning […]
PyCon 2020
Hey Folks! I finally got around to watching a bunch of the talks and found several of the talks useful for improving my Python coding skills in general and/or in the context of doing Data Science. Here are some interesting talks from PyCon 2020: Beautiful Python Refactoring video. The talk was simple but powerful in demonstrating […]
Using Classes in Python
We know that you’ve probably heard of object oriented programming (OOP), but outside of designing games, when is best to use it for data science? We haven’t used OOP much until recently when we refactored a data science project code base. In this article, we give a brief refresher for OOP and discuss our top […]
Staying Up-To-Date on AI/ML
Great Email Newsletters on AI/ML All newsletters are released weekly. Import AI – AI newsletter that summarizes recent news articles and research; I enjoy how honest and succinct this newsletter is; also like that the implications of new algorithms are always discussed by Jack, who is an advocate for improved ML model explicability and data […]
R vs Python
I generally reach for Python when building data science pipelines, however I discovered R before I decided to invest in learning Python. R has saved me lots of time when it came to quickly and easily preparing nice-looking plots for research. It begs the question of where is R better than Python for certain purposes? […]
Building Recommendations Systems
Recommendations systems are good for matching users to their favorite products and are incredibly popular. In fact you have likely used a recommendation system at least once in your life. For example, Amazon uses recommendation systems to suggest new exciting products to purchase based on users’ previous purchase patterns and those similar users. Netflix also […]
Recurrent Neural Networks in PyTorch
Feed forward networks cannot learn from the past, but Recurrent Neural Networks (RNNs) can learn by accepting data in a sequence. Examples of applications for RNNs include the text autocomplete feature on your phone and performing language translations. Recurrent Neurons (RNs) act as the building blocks of RNNs. The difference between RNs and feed forward […]