It is always nice to process the data using modern tools like Pandas or Jupyter. But let’s imagine the case when a colleague or friend asks to make a data analysis, but he or she is not a technical person, does not use Python or Jupyter, and does no- 26363Murphy2025-03-23
| ARTIFICIAL INTELLIGENCE| LLM| NLP | LLMs have shown their skills in recent months, demonstrating that they are proficient in a wide variety of tasks. All this through one mode of interaction: prompting. In recent months there has been a rush to broaden- 28956Murphy2025-03-23
Identifying the top 5% of candidates via Technical Problem Framing- 20634Murphy2025-03-23
LLM Applications are dataflows, use a tool specifically designed to express them.- 24393Murphy2025-03-23
Working with Pandas and dlisio to Explore Well Log Data- 23688Murphy2025-03-23
Two case studies with parameter estimation and input function calibration- 29843Murphy2025-03-23
Despite their popularity amongst users of R, Python and Julia, Jupyter Notebooks are rarely used to their full potential. Most users know the basic commands (execute code, comment, save, etc.), but few make use of Jupyter’s hidden tricks – eve- 22149Murphy2025-03-23
Learn to effectively optimize hyperparameters, and prevent creating overtrained models for XGBoost, CatBoost, and LightBoost- 21912Murphy2025-03-23
Hands-on tutorial on how to build your first sentiment analysis model using BERT- 28771Murphy2025-03-23
Understanding and Mitigating Model Collapse- 28495Murphy2025-03-23
As most of you already know, Python is a general-purpose programming language optimized for simplicity and ease of use. While it’s a great tool for light tasks, code execution speed can soon become a major bottleneck in your programs. In this articl- 20613Murphy2025-03-23
Another way to efficiently host and scale your LLMs with Amazon SageMaker- 20859Murphy2025-03-23
Araucana XAI: Local Explainability With Decision Trees for Healthcare Introducing a new model-agnostic, post hoc XAI approach based on CART to provide local explanations improving the transparency of AI-assisted decision making in healthcare Why did AI ge- 21472Murphy2025-03-23
Time to supercharge your data analysis workflow- 24400Murphy2025-03-23
"Amidst the noise of the crowd, it’s the softly spoken words that hold the hidden wisdom- 25692Murphy2025-03-23
Reducing the dimension of a dataset using methods such as PCA- 21515Murphy2025-03-23
Generative AI has already started shaking the world of Data Governance, and it is set to keep doing so. It’s just been 6 months since ChatGPT’s release, but it feels like we need a retrospective already. In this piece, I’ll explore how g- 25470Murphy2025-03-23
A demo of GPBoost in Python & R using real-world data- 26418Murphy2025-03-23
How to make reliable calls to ChatGPT API to build robust applications- 28715Murphy2025-03-23
Estimate the distance between any pair of sites from their geographical coordinates as a stepping stone to solving routing problems- 29873Murphy2025-03-23
Why is ChatGPT only trained up until 2021?
Learn how to rearrange your code to achieve significant speed improvements.
