Depends on the definition

“Depends on the definition” delves into the intricacies of measuring speed in Python programming, exploring the trade-offs between development time and run time efficiency. The podcast episode discusses the significance of understanding what aspects of Python performance are being evaluated and how they impact productivity. Additionally, the document touches on the importance of short-term memory in AI applications, specifically focusing on maintaining conversation context for seamless interactions. The conversation also highlights the use of PostgreSQL databases in conjunction with Langchain for persistent memory storage. Overall, the sources provide insights into optimizing Python programming speed and enhancing AI application functionalities.

Visit Depends on the definition

Work with me

I’m a freelancer I have a passion to support companies to solve their business challenges by leveraging their data and applying advanced machine learning. Services offered Hands on teaching/consulting...

Privacy

Depends on the definition

It is Depends-on-the-definition’s policy to respect your privacy regarding any information we may collect while operating our website. This Privacy Policy applies to https://depends-on-the-definition....

Legal details

Depends on the definition

Contact E-Mail: info@depends-on-the-definition.com Accountability for content The contents of our pages have been created with the utmost care. However, we cannot guarantee the contents’ accuracy, com...

About

Depends on the definition

the author Hi, my name is Tobias. I’m a trained mathematician that now works as a data scientist and machine learning engineer. In 2018 I achieved the status of a kaggle master and still spend quite s...

Causal graphs and the back-door criterion - A practical test on deconfounding

Depends on the definition

I read into causal inference recently and since I didn’t really have a use-case for it right now, I played around with some data and some causal graphs. In this article, I looked at some causal graphs...

How to calculate shapley values from scratch

Depends on the definition

The shapley value is a popular and useful method to explain machine learning models. The shapley value of a feature is the average contribution of a feature value to the prediction. In this article I’...

How to add new tokens to huggingface transformers vocabulary

Depends on the definition

In this short article, you’ll learn how to add new tokens to the vocabulary of a huggingface transformer model.

How to test error messages with pytest

Depends on the definition

In this short article, you will learn, how and when to test the error message of an exception with pytest.

Learning unsupervised embeddings for textual similarity with transformers

Depends on the definition

In this article, we look at SimCSE, a simple contrastive sentence embedding framework, which can be used to produce superior sentence embeddings, from either unlabeled or labeled data. The idea behind...

The missing guide on data preparation for language modeling

Depends on the definition

Language models gained popularity in NLP in the recent years. Sometimes you might have enough data and want to train a language model like BERT or RoBERTa from scratch. While there are many tutorials ...

Data augmentation with transformer models for named entity recognition

Depends on the definition

In this article we sample from pre-trained transformers to augment small, labeled text datasets for named entity recognition.

How to approach almost any real-world NLP problem

Depends on the definition

This time, I’m going to talk about how to approach general NLP problems. But we’re not going to look at the standard tips which are tosed around on the internet, for example on platforms like kaggle.