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Understanding Graph Neural Network with hands-on example| Part-1

Becoming Human

This post includes the fundamentals of graphs, combining graphs and deep learning, and an overview of Graph Neural Networks and their applications. Through the next series of this post here , I will try to make an implementation of Graph Convolutional Neural Network. A typical application of GNN is node classification.

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Announcing our $50M Series C to build superhuman Speech AI models

AssemblyAI

Over the past two years, we’ve seen the combination of bigger datasets, better compute, and new neural network architectures like the Transformer make possible the significant advancement of AI models across nearly every modality — and make our vision of building superhuman Speech AI models more achievable than ever before.

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This AI Paper Unveils X-Raydar: A Groundbreaking Open-Source Deep Neural Networks for Chest X-Ray Abnormality Detection

Marktechpost

Trained on a dataset from six UK hospitals, the system utilizes neural networks, X-Raydar and X-Raydar-NLP, for classifying common chest X-ray findings from images and their free-text reports. The X-Raydar achieved a mean AUC of 0.919 on the auto-labeled set, 0.864 on the consensus set, and 0.842 on the MIMIC-CXR test.

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Introduction to Graph Neural Networks

Heartbeat

Photo by Resource Database on Unsplash Introduction Neural networks have been operating on graph data for over a decade now. Neural networks leverage the structure and properties of graph and work in a similar fashion. Graph Neural Networks are a class of artificial neural networks that can be represented as graphs.

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Top TensorFlow Courses

Marktechpost

Learning TensorFlow enables you to create sophisticated neural networks for tasks like image recognition, natural language processing, and predictive analytics. It covers various aspects, from using larger datasets to preventing overfitting and moving beyond binary classification.

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TinyML: Applications, Limitations, and It’s Use in IoT & Edge Devices

Unite.AI

Also, in the current scenario, the data generated by different devices is sent to cloud platforms for processing because of the computationally intensive nature of network implementations. To tackle the issue, structured pruning and integer quantization for RNN or Recurrent Neural Networks speech enhancement model were deployed.

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Taming Long Audio Sequences: Audio Mamba Achieves Transformer-Level Performance Without Self-Attention

Marktechpost

Audio classification has evolved significantly with the adoption of deep learning models. Initially dominated by Convolutional Neural Networks (CNNs), this field has shifted towards transformer-based architectures, which offer improved performance and the ability to handle various tasks through a unified approach.