Before caffeine, my brain works just like a beginner AI. Mostly confused. Always scanning.

A short (fun) article on computer vision.

open.substack.com/pub/dianawol...

#computervision #aivision #aiexplained #ConvolutionalNeuralNetworks #alexnet #convolution

Meta’s star AI scientist Yann LeCun plans to leave for own startup https://arstechni.ca #convolutionalneuralnetworks #FundamentalAIResearch #largelanguagemodels #AIdevelopmenttools #NewYorkUniversity #AIinfrastructure #machinelearning #MarkZuckerberg #AlexandrWang #deeplearning #AIalignment…

Understanding Neural Networks: A Simple Guide for 2024 The Brains Behind the Buzz: A No-Nonsense Guide to Understanding Neural Networks Ever wonder how your phone instantly recognizes your face? Or how Netflix just *knows* you’d love that obscure…

Understanding Neural Networks: A Simple Guide for 2024 #feedforwardneuralnetwork #artificialintelligencebasics #recurrentneuralnetworks #activationfunctions #neuralnetworklayers #AIforbeginners #typesofneuralnetworks #convolutionalneuralnetworks #machinelearningmodels #backpropagationexplained

Understanding Neural Networks: A Beginner's Guide The Brains Behind the Buzz: A No-Nonsense Guide to Understanding Neural Networks You hear the terms all the time. Artificial intelligence. Machine learning. Deep learning. They're behind the Netflix shows…

Understanding Neural Networks: A Beginner's Guide #hiddenlayers #machinelearningmodels #convolutionalneuralnetworks #backpropagationsimpleexplanation #deeplearningexplained #recurrentneuralnetworks #artificialneurons #activationfunctions #artificialintelligencebasics #neuralnetworktraining

Patient-Specific CNN-RNN for Lung-Sound Detection With 4× Smaller Memory

Log-quant cuts model memory 4× with minimal accuracy loss; compute fits mobile SoCs, enabling real-time, wearable lung-sound screening. #convolutionalneuralnetworks

Dataset, Features, Model, and Quantization Strategy for Respiratory Sound Classification

Mel-spectrograms feed a CNN-RNN; last layers retrain on tiny patient sets, then log-quant weights trim memory 4× for wearables. #convolutionalneuralnetworks

Patient-Specific CNN-RNN for Wheeze and Crackle Detection with 4× Memory Savings

Hybrid spectrogram CNN-RNN outperforms VGG/MobileNet and shows that transfer learning solves data scarcity in respiratory AI. #convolutionalneuralnetworks

Lessons from Testing AI Models on Global Damage Data

This article’s results show Faster R-CNN with ResNet backbones beats YOLOv5 for road damage detection, with noted gains and failure case insights. #convolutionalneuralnetworks

Potholes, Pipelines, and Precision: Benchmarking Object Detectors for Global Road Safety

This article compares YOLOv5 and Faster R-CNN for road damage detection, finding two-stage models with ResNet backbones yield top generalized results. #convolutionalneuralnetworks

Benchmarking Faster R-CNN and YOLOv5 for Global Road Damage Detection Across Countries

This article details model choice, tuning, and dataset prep for road damage detection, comparing Faster R-CNN and YOLOv5 on a global multi-country dataset. #convolutionalneuralnetworks

More Than a Feeling: Visualizing Why Filter Atoms Outsmart LoRA in Fine-Tuning

VTAB-1k results show our method beats LoRA in few-shot tasks, with Grad-CAM visuals confirming better spatial awareness and superior generalization. #convolutionalneuralnetworks

Tuning the Pixels, Not the Soul: How Filter Atoms Remake ConvNets

This method fine-tunes ConvNets by adapting only filter atoms, preserving pre-trained knowledge for efficient, high-performance transfer learning. #convolutionalneuralnetworks

Keep the Channel, Change the Filter: A Smarter Way to Fine-Tune AI Models

Fine-tune models by adapting only spatial filter atoms, preserving channel knowledge to prevent overfitting for superior, parameter-efficient results. #convolutionalneuralnetworks

Keep the Channel, Change the Filter: A Smarter Way to Fine-Tune AI Models

Fine-tune models by adapting only spatial filter atoms, preserving channel knowledge to prevent overfitting for superior, parameter-efficient results. #convolutionalneuralnetworks

from https://pytorch.org/hub/pytorch_vision_resnet/

#ConvolutionalNeuralNetworks ( #CNNs in short) are immensely useful for many #imageProcessing tasks and much more in #DeepLearning.

Yet you sometimes encounter some bits of code with little explanation. Have you ever wondered about the origins of the values for image normalization in #imagenet ?

Machine Vision Unveiled: How AI Sees—and What It Means A radiologist examines a complex brain scan, carefully studying each detail. In the same moment, an AI system has already mapped the entire image, highlighting four potential areas of concern—all in l...

www.linkedin.com/pulse/machin...

#deeplearning #ConvolutionalNeuralNetworks #CNNs #deeplearningwiththewolf

All those long excavation evenings sorting #pottery ... why not teaching computers how to do it (by means of #ConvolutionalNeuralNetworks)? 🤔🏺

www.eurekalert.org/pub_releases/2021-05/nau... via @EurekAlert