Deep Learning

In this tutorial, we will explore Physics Informed Neural Networks (PINNs), which are neural networks trained to solve supervised learning tasks while respecting given laws of physics described by general nonlinear partial differential equations.

Deep Neural Network Implementation Using PyTorch - Implementing all the layers In this tutorial, we will explore the various layers available in the torch.nn module. These layers are the building blocks of neural networks and allow us to create complex architectures for different tasks.

Advanced Cancer Classification Repository. The-Health-and-Medicine-OxML-competition-track

Introduction 🚀 Dive into the Exciting World of Deep Neural Networks with PyTorch! 🤖🔥 Hey there, fellow tech enthusiast! 🤓 Ever felt like PyTorch is a bit of a puzzle, unlike its more user-friendly counterparts?

This comprehensive review article surveys recent advancements in deep learning applications and methods for autonomous navigation. We …

Generalizable end-to-end deep learning frameworks for real-time attitude estimation using 6DoF inertial measurement units

• End-to-end learning framework for real-time inertial attitude estimation. Generalized across various sampling rates. RNN-CNN networks employed to learn motion characteristics, noise, and bias. Proposed approach outperforms traditional algorithms and other deepOutperforms traditional algorithms in terms of accuracy up to 40 Evaluated using seven datasets, totaling 120 h and 200 kilometers of IMU measurements.