Deep Learning Made Easier with this low-code Declarative Framework- 23492Murphy2025-03-23
A Vision Transformer-based model- 29925Murphy2025-03-23
The Top 5 Features for Efficient Data Manipulation- 21742Murphy2025-03-23
An examination of the factors that contribute to a good recommendation and long-term user retention- 23654Murphy2025-03-23
Once you deploy an NLP or LLM-based solution, you need a way to keep tabs on it. But how do you monitor unstructured data to make sense of the pile of texts? There are a few approaches here, from detecting drift in raw text data and embedding drift to usi- 24664Murphy2025-03-23
Why perfect predictors result in a p-value of 0.93 ?- 25843Murphy2025-03-23
Concepts and Ideas- 26020Murphy2025-03-23
A CNN-based model- 24703Murphy2025-03-23
Tilted Icelandic iceberg (image by the author) Measurement is the cornerstone of all science. Without it, how could we test our hypotheses? Python, as the preeminent programming language for data science, makes it easy to gather, clean, and make sense of- 21399Murphy2025-03-23
A step-by-step tutorial to build and run Python Wheel Tasks on custom Docker images in Databricks (feat. Poetry and Typer CLI)- 25734Murphy2025-03-23
How long does it take to reach a specific value?- 27478Murphy2025-03-23
Depthwise separable convolutions- 27582Murphy2025-03-23
Essay A discussion at the frontier between science and fiction. Human intelligence, with its extraordinary cognitive capabilities, stands unparalleled among other species. The catalysts behind this intellectual supremacy can be traced back to the advent o- 28797Murphy2025-03-23
Part 1: Principles from Test-Driven Development- 28765Murphy2025-03-23
In this article we will follow the tenets of TDD for developing Scientific Software as laid out in the first installment of this series to develop an edge detection filter known as the Sobel filter. In the first article, we talked about how important R- 20901Murphy2025-03-23
How to find the explanation for every anomaly on your metrics- 22139Murphy2025-03-23
Understanding cross-validation and applying it in practical daily work is a must-have skill for every data scientist. While the primary purpose of cross-validation is to assess model performance and fine-tune hyperparameters, it offers additional outputs- 28760Murphy2025-03-23
Exploring how to train models and generate sounds with audio waveform diffusion on a consumer laptop and GPU with less than 2GB VRAM- 29274Murphy2025-03-23
Portfolios and LinkedIn profiles will only get you so far in your career as a Data Scientist. Sure – LinkedIn is a great way to build a professional network, and portfolios provide a great way to showcase the cool stuff you’ve done. But if no- 24314Murphy2025-03-23
Enhancing your Python projects with robust retry mechanisms and error-handling techniques- 26121Murphy2025-03-23
Why is ChatGPT only trained up until 2021?
Learn how to rearrange your code to achieve significant speed improvements.
