Jianwei Shuai's team and Jiahuai Han's team at Xiamen University have developed a deep autoencoder-based data-independent acquisition data analysis software for protein mass spectrometry, which ...

Learn more about the relationship between Machine Learning vs Deep Learning vs Foundation Models and how they effect AI models working as a ...

Compared to using PCA for dimensionality reduction, using a neural autoencoder has the big advantage that it works with source data that contains both numeric and categorical data, while PCA works ...

Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...

Methods A multimodal deep-learning model with transformers was developed for real-time recurrence prediction using baseline clinical, pathological, and molecular data with longitudinal laboratory and ...

The recently published book Understanding Deep Learning by [Simon J. D. Prince] is notable not only for focusing primarily on the concepts behind Deep Learning — which should make it highly a… ...

The proposed deep learning–based approach for body composition analysis demonstrated comparable performance to the manual process, presenting a more cost-effective alternative to conventional methods.

A Deep Learning Alternative Can Help AI Agents Gameplay the Real World A new machine learning approach tries to better emulate the human brain, in hopes of creating more capable agentic AI.

Linux supports a wide array of AI and ML frameworks that cater to different aspects of machine learning, from deep learning to statistical modeling. Below are some of the most popular frameworks ...