fbpx
How to Pivot and Plot Data With Pandas
A big challenge of working with data is manipulating its format for the analysis at hand. To make things a bit more difficult, the “proper format” can depend on what you are trying to analyze, meaning we have to know how to melt, pivot, and transpose... Read more
First Steps Before Applying Reinforcement Learning for Trading
There are many methodologies in algorithmic trading — from automated trade entry and close points based on technical and fundamental indicators to intelligent forecasts and decision making using complex maths and, of course, artificial intelligence. Reinforcement learning here stands out as a Holy Grail — no... Read more
Audio-Visual Speech Enhancement and Separation Based on Deep Learning
Imagine being at a cocktail party with your friends. Although you are surrounded by other people having their own conversations and a music band is playing in the background, you are still able to engage with your friends and understand what they are saying. The reason... Read more
Saying Hello to DataFrames.jl
A majority of data scientists use Python or R to perform data preparation tasks before jumping to modeling. The Julia language is a younger player in this field that promises that you will be able to do the number-crunching-intensive parts of your pipelines fast. However, the... Read more
Never Wait for a Job to Start Working in Data Science
You finally finished that college CS degree, completed hundreds of hours of training online, got your certifications, or simply have the skills you need to transition. The bottom line is, you are ready to start working in data science, an incredibly exciting field. And then, you... Read more
Promoting the Responsible Use of AI in Health Care
Artificial intelligence (AI) holds tremendous promise as a means of improving the efficiency and quality of health care delivery— from enhancing patient outreach and engagement, to managing medical and pharmacy inventory, to identifying patients at the greatest risk of disease progression. The tangible benefits of AI... Read more
Fast, Visual, and Explainable ML Modeling With PerceptiLabs
Pure-code ML frameworks like TensorFlow, have become popular for building ML models because they effectively offer a high-level grammar for describing model topologies and algorithms. This is a powerful approach, but it has limitations for providing insight and explainability of models. These issues are further magnified... Read more
Making Explainability Work in Practice
Complex ‘black box’ models are becoming more and more prevalent in industries involving high-stakes decisions (such as finance, healthcare, insurance). As machine learning algorithms take a prominent role in our daily lives, explaining their decision will only grow in importance via explainability. By now there is... Read more
The Importance of Industry 4.0 and AI Adoption in a Changing Industry
I don’t need to tell you how much the world has changed over the last year – a (hopefully) once-in-a-lifetime pandemic took over our lives and caused massive disruption around the world. The way we live, work, and interact with each other was completely flipped on... Read more
Why Causal Machine Learning is the Next Revolution in AI
Editor’s note: Robert Ness is a speaker for ODSC East 2021. Check out his talk, “Causal Machine Learning Blitz,” there! Causal modeling and inference are perhaps at the core of the most interesting questions in data science. A common task for a data scientist at a... Read more