👉 Complete articles on Geometric Deep Learning, Graph Neural Networks, Topological Data Analysis with exercises are available on my Substack newsletter Hands-on Geometric Deep Learning The authors ...

Deep Learning Crash Course: A Hands-On, Project-Based Introduction to Artificial Intelligence is written by Giovanni Volpe, Benjamin Midtvedt, Jesús Pineda, Henrik Klein Moberg, Harshith Bachimanchi, ...

NIF is a mesh-agnostic dimensionality reduction paradigm for parametric spatial temporal fields. For decades, dimensionality reduction (e.g., proper orthogonal decomposition, convolutional ...

Books provide a deeper understanding of AI concepts beyond running code or tutorials. Hands-on examples and practical exercises make learning neural networks and deep learning easier. Exploring ethics ...

This repo contains all my Deep Learning semester work, including implementations of FNNs, CNNs, autoencoders, CBOW, and transfer learning. I explored TensorFlow, Keras, PyTorch, and Theano while ...

A new Ph.D. program in the physics department designed for students who want to be at the forefront of cosmic discovery, data science and interdisciplinary research. The Mellon College of Science is ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Mass spectrometry is a ubiquitous technique capable of complex chemical analysis. The ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Non-Commercial (NC): Only non-commercial uses of the work are permitted. No ...

I hope you had a successful start to the new year, as did AI and deep learning research. In this edition of Ahead of AI #5, I wanted to showcase recent advancements in computer vision rather than ...

Machine learning (ML) approaches are a collection of algorithms that attempt to extract patterns from data and to associate such patterns with discrete classes of samples in the data—e.g., given a ...