Categories: AI/ML News

New study finds bigger datasets might not always be better for AI models

From ChatGPT to DALL-E, deep learning artificial intelligence (AI) algorithms are being applied to an ever-growing range of fields. A new study from University of Toronto Engineering researchers, published in Nature Communications, suggests that one of the fundamental assumptions of deep learning models—that they require enormous amounts of training data—may not be as solid as once thought.

Study explores the scaling of deep learning models for chemistry research

Deep neural networks (DNNs) have proved to be highly promising tools for analyzing large amounts of data, which could speed up research in various scientific fields. For instance, over the past few years, some computer scientists have trained models based on these networks to analyze chemical data and identify promising…

November 12, 2023

In "AI/ML News"

Use PyTorch Deep Learning Models with scikit-learn

The most popular deep learning libraries in Python for research and development are TensorFlow/Keras and PyTorch, due to their simplicity. The scikit-learn library, however, is the most popular library for general machine learning in Python. In this post, you will discover how to use deep learning models from PyTorch with…

February 9, 2023

In "AI/ML Research"

PyTorch Tutorial: How to Develop Deep Learning Models with Python

Last Updated on May 1, 2023 Predictive modeling with deep learning is a skill that modern developers need to know. PyTorch is the premier open-source deep learning framework developed and maintained by Facebook. At its core, PyTorch is a mathematical library that allows you to perform efficient computation and automatic…

May 1, 2023

In "AI/ML Research"

AI Generated Robotic Content