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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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Top TensorFlow Courses

Marktechpost

TensorFlow is a powerful open-source framework for building and deploying machine learning models. Learning TensorFlow enables you to create sophisticated neural networks for tasks like image recognition, natural language processing, and predictive analytics.

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

Unite.AI

In the past few years, Artificial Intelligence (AI) and Machine Learning (ML) have witnessed a meteoric rise in popularity and applications, not only in the industry but also in academia. Recent research in the field of IoT edge computing has demonstrated the potential to implement Machine Learning techniques in several IoT use cases.

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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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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.

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Train and host a computer vision model for tampering detection on Amazon SageMaker: Part 2

AWS Machine Learning Blog

In the first part of this three-part series, we presented a solution that demonstrates how you can automate detecting document tampering and fraud at scale using AWS AI and machine learning (ML) services for a mortgage underwriting use case. The VGG-16 consists of 13 convolutional layers and three fully connected layers.