The Future of Data Science – Mining GTC 2021 for Trends
Deep learning enthusiasts are increasingly putting NVIDIA’s GTC at the top of their gotta-be-there conference list. I enjoyed mining this year’s talks for...
Enterprise-class NLP with spaCy v3
spaCy is a python library that provides capabilities to conduct advanced natural language processing analysis and build models that can underpin document analysis,...
How to supercharge data exploration with Pandas Profiling
Producing insights from raw data is a time-consuming process. Predictive modeling efforts rely on dataset profiles, whether consisting of summary statistics or descriptive...
PyCaret 2.2: Efficient Pipelines for Model Development
Data science is an exciting field, but it can be intimidating to get started, especially for those new to coding. Even for experienced...
Code
The Curse of Dimensionality
Guest Post by Bill Shannon, Founder and Managing Partner of BioRankings Danger of Big Data Big data is the rage. This could be...
The importance of structure, coding style, and refactoring in notebooks
Notebooks are increasingly crucial in the data scientist's toolbox. Although considered relatively new, their history traces back to systems like Mathematica and MATLAB....
Machine Learning
Deep Learning Illustrated: Building Natural Language Processing Models
Many thanks to Addison-Wesley Professional for providing the permissions to excerpt "Natural Language Processing" from the book, Deep Learning Illustrated by Krohn, Beyleveld,...
Make Machine Learning Interpretability More Rigorous
This Domino Data Science Field Note covers a proposed definition of machine learning interpretability, why interpretability matters, and the arguments for considering a...
Practical Techniques
Faster data exploration in Jupyter through Lux
Notebooks have become one of the key primary tools for many data scientists. They offer a clear way to collaborate with others throughout...
Performing Non-Compartmental Analysis with Julia and Pumas AI
When analysing pharmacokinetic data to determine the degree of exposure of a drug and associated pharmacokinetic parameters (e.g., clearance, elimination half-life, maximum observed...
Leaders at Work
Fireside Chat: Stig Pedersen from Topdanmark
"In having one or two very successful algorithmic deployments, the business then begins coming to you to ask for assistance. It becomes a...
Defining clear metrics to drive model adoption and value creation
One of the biggest ironies of enterprise data science is that although data science teams are masters at using probabilistic models and diagnostic...
The Role of Containers on MLOps and Model Production
Container technology has changed the way data science gets done. The original container use case for data science focused on what I call,...
HyperOpt: Bayesian Hyperparameter Optimization
This article covers how to perform hyperparameter optimization using a sequential model-based optimization (SMBO) technique implemented in the HyperOpt Python package. There is...
Deep Reinforcement Learning
This article provides an excerpt "Deep Reinforcement Learning" from the book, Deep Learning Illustrated by Krohn, Beyleveld, and Bassens. The article includes an...
Towards Predictive Accuracy: Tuning Hyperparameters and Pipelines
This article provides an excerpt of “Tuning Hyperparameters and Pipelines” from the book, Machine Learning with Python for Everyone by Mark E. Fenner....