Google has reportedly initiated the TorchTPU project to enhance support for the PyTorch machine learning framework on its tensor processing units (TPUs), aiming to challenge the software dominance of ...

Google's TorchTPU aims to enhance TPU compatibility with PyTorch Google seeks to help AI developers reduce reliance on Nvidia's CUDA ecosystem TorchTPU initiative is part of Google's plan to attract ...

CNN in deep learning is a special type of neural network that can understand images and visual information. It works just like human vision: first it detects edges, lines and then recognizes faces and ...

This project implements ResNet-50, a deep convolutional neural network with 50 layers that uses residual connections to enable training of very deep networks. The architecture includes identity ...

Abstract: We introduce a new convolutional autoencoder architecture for user modeling and recommendation tasks with several improvements over the state of the art. First, our model has the flexibility ...

SVG Autoencoder - Uses a frozen representation encoder with a residual branch to compensate the information loss and a learned convolutional decoder to transfer the SVG latent space to pixel space.

Abstract: A fault detection method for power conversion circuits using thermal images and a convolutional autoencoder is presented. The autoencoder is trained on thermal images captured from a ...

Learn how the Inception Net V1 architecture works and how to implement it from scratch using PyTorch. Perfect for deep learning enthusiasts wanting a hands-on understanding of this classic ...

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