For many organizations, a major question faced by data scientists and engineers is how best to go from the experimentation stage to production. In this first part of six, we outline the most important part of any enterprise data science analysis – posing a business problem.
Paperspace contributor Nigama Vykari guides us through use of the Hough transform feature extraction technique in the context of lane detection for self-driving cars.
In the first part of this new series we'll explore basics of audio analysis and signal processing and we'll learn to apply basic machine learning techniques to audio.
Aitextgen is a Python library for training text-generation models using GPT-2 and GPT-3/GPT Neo. In this tutorial you'll get aitextgen up and running quickly in a Jupyter notebook on a free GPU instance from Paperspace Gradient!
This article covers a deeper level understanding of Question Answering models in NLP, the datasets commonly used, and how to choose a pre-trained model by considering various factors like the document structure, runtime cost, etc.
