Exploring the Moral and Ethical Perspective of a Dataset while Building an Explainable AI Solution
Developing AI code in the 2010s relied on knowledge and talent. Developing AI code in the 2020s implies the accountability of XAI for every aspect of an AI project. It includes moral, ethical, legal, and technical perspectives, all for building an explainable AI solution. [Related article:... Read more
Julia for Management/Analysis of Johns Hopkins COVID Data
Like many analytics geeks, I’ve been tracking data on the Covid pandemic since early spring. My source is the Center for Systems Science and Engineering at Johns Hopkins University, with files for download made available at midnight Central time. I’ve established a pretty significant R infrastructure in JupyterLab to... Read more
Dask in the Cloud
When doing data science and/or machine learning, it is becoming increasingly common to need to scale up your analyses to larger datasets. When working in Python and the PyData ecosystem, Dask is a popular tool for doing so. There are many reasons for this, one being... Read more
Coiled: Dask for Everyone, Everywhere
Data scientists increasingly solve large machine learning and data problems with Python. But historically Python struggled with parallel computing. This led many of us in the community to make Dask, a library for parallel computing and data science for Python. Dask has been a go-to solution... Read more
How to Deal with Files in Google Colab: Everything You Need to Know
Google Colaboratory is a free Jupyter notebook environment that runs on Google’s cloud servers, letting the user leverage backend hardware like GPUs and TPUs. This lets you do everything you can in a Jupyter notebook hosted in your local machine, without requiring the installations and setup for... Read more
How to Make Sense of the Reinforcement Learning Agents? What and Why I Log During Training and Debug
Based on simply watching how an agent acts in the environment, it is hard to tell anything about why it behaves this way and how it works internally. That’s why it is crucial to establish metrics that tell WHY the agent performs in a certain way.... Read more
Pruning for Success
Pruning is an older concept in the deep learning field, dating back to Yann LeCun’s 1990 paper Optimal Brain Damage. It has recently gained a lot of renewed interest, becoming an increasingly important tool for data scientists. The ability to deploy significantly smaller and faster models... Read more
Building an AI Assistant for the Moonbase with DeepPavlov.ai
Today, every AI assistant is in their infancy. While there were some exciting developments in the predecessor of Siri, DARPA CALO, today’s best and brightest assistants are quite far from what was envisioned in the science fiction books of the Golden Age. Why is it so? ... Read more
Dealing with the Incompleteness of Machine Learning
The prospect of automating every aspect of human life is exciting. Imagine humans permanently living a life of leisure and machine learning robot labor picking up the slack! Even though this sounds like a recipe for lazy and depressed humans, we can still be useful to... Read more
K-Nearest Neighbour (KNN) Algorithms is an easy-to-implement & advanced level supervised machine learning algorithm used for both – classification as well as regression problems. However, you can see a wide of its applications in classification problems across various industries. If you’ve been shopping a lot in... Read more