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Introduction to Supervised Deep Learning Algorithms!

Analytics Vidhya

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction This article aims to explain deep learning and some supervised. The post Introduction to Supervised Deep Learning Algorithms! appeared first on Analytics Vidhya.

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Deep Residual Learning for Image Recognition (ResNet Explained)

Analytics Vidhya

Introduction Deep learning has revolutionized computer vision and paved the way for numerous breakthroughs in the last few years. One of the key breakthroughs in deep learning is the ResNet architecture, introduced in 2015 by Microsoft Research.

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NeRFs Explained: Goodbye Photogrammetry?

PyImageSearch

Home Table of Contents NeRFs Explained: Goodbye Photogrammetry? Block #A: We Begin with a 5D Input Block #B: The Neural Network and Its Output Block #C: Volumetric Rendering The NeRF Problem and Evolutions Summary and Next Steps Next Steps Citation Information NeRFs Explained: Goodbye Photogrammetry? How Do NeRFs Work?

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Damian Bogunowicz, Neural Magic: On revolutionising deep learning with CPUs

AI News

AI News spoke with Damian Bogunowicz, a machine learning engineer at Neural Magic , to shed light on the company’s innovative approach to deep learning model optimisation and inference on CPUs. One of the key challenges in developing and deploying deep learning models lies in their size and computational requirements.

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AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the difference?

IBM Journey to AI blog

To keep up with the pace of consumer expectations, companies are relying more heavily on machine learning algorithms to make things easier. How do artificial intelligence, machine learning, deep learning and neural networks relate to each other? Machine learning is a subset of AI. What is machine learning?

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Top Books on Deep Learning and Neural Networks

Marktechpost

Deep learning is crucial in today’s age as it powers advancements in artificial intelligence, enabling applications like image and speech recognition, language translation, and autonomous vehicles. Additionally, it offers insights into the diverse range of deep learning techniques applied across various industrial sectors.

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Deep Learning Meets Cybersecurity: A Hybrid Approach to Detecting DDoS Attacks with Unmatched Accuracy

Marktechpost

Others have proposed a hybrid network intrusion detection system integrating convolutional neural networks (CNN), fuzzy C-means clustering, genetic algorithm, and a bagging classifier. By hybridizing optimization techniques with deep belief networks, the method aims to enhance DDoS attack detection accuracy, speed, and scalability.