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Monitoring A Convolutional Neural Network (CNN) in Comet

Heartbeat

Tracking your image classification experiments with Comet ML Photo from nmedia on Shutterstock.com Introduction Image classification is a task that involves training a neural network to recognize and classify items in images. Before being fed into the network, the photos are pre-processed and shrunk to the same size.

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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.

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Revolutionizing Autonomy: CNNs in Self-Driving Cars

Towards AI

Photo by Erik Mclean on Unsplash This article uses the convolutional neural network (CNN) approach to implement a self-driving car by predicting the steering wheel angle from input images of three front cameras in the car’s center, left, and right. Levels of Autonomy. [3] Yann LeCun et al., Yann LeCun et al.,

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Top TensorFlow Courses

Marktechpost

Modules include building neural networks with Keras, computer vision, natural language processing, audio classification, and customizing models with lower-level TensorFlow code. It covers various aspects, from using larger datasets to preventing overfitting and moving beyond binary classification.

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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. So, let’s get started! What are Graphs?

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

Unite.AI

The implementation of TinyML for computer vision based application on edge platforms required developers to overcome the major challenge of CNN or Convolutional Neural Networks with a high generalization error, and high training & testing accuracy.

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Introduction to Graph Neural Networks

Heartbeat

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. Edge-level tasks , on the other hand, entail edge classification and link prediction.