ai-Paul E · @paul_ekwereii
31 followers · 53 posts · Server sigmoid.socialI experimented with using Large Language Models to solve a complex #imagerecognition problem.
The generated machine learning model by ChatGPT using a few prompts was able to detect #MNIST handwritten digits with an accuracy of 98%.
Read on if you want to learn how I did this.
#AI #artificialintelligence #deeplearning #neuralnetworks #bingai #bingchat #convolutionalneuralnetworks #LLMs #computervision
#imagerecognition #mnist #ai #artificialintelligence #deeplearning #neuralnetworks #bingai #bingchat #convolutionalneuralnetworks #LLMs #computervision
CryptoNewsBot · @cryptonewsbot
506 followers · 24312 posts · Server schleuss.onlineWhat are convolutional neural networks? - Convolutional neural networks (CNNs) are a class of deep neural n... - https://cointelegraph.com/explained/what-are-convolutional-neural-networks #convolutionalneuralnetworks #cnns
#cnns #convolutionalneuralnetworks
Randy Edmonds · @apphands
52 followers · 74 posts · Server mastodon.social
Dominic Boutet · @dominic_boutet
552 followers · 55 posts · Server neuromatch.social
"Emergence of brain-like mirror-symmetric viewpoint tuning in convolutional neural networks"
https://www.biorxiv.org/content/10.1101/2023.01.05.522909v1
#Neuroscience #Neuro #ComputationalNeuroscience #Vision #MachineLearning #DeepLearning #ConvolutionalNeuralNetworks
#arxivfeed #neuroscience #neuro #computationalneuroscience #vision #machinelearning #deeplearning #convolutionalneuralnetworks
Stefan Contractor · @stefancontracto
26 followers · 2 posts · Server mastodon.au
Neelesh Rampal from NIWA teaches us the difference between #linearregression and #convolutionalneuralnetworks #AMOS2022
#linearregression #convolutionalneuralnetworks #amos2022
Marko Jahnke · @markojahnke
66 followers · 227 posts · Server bonn.socialAbseits dieser Ausstellung gibt es ein (offenbar noch nicht dokumentiertes) interaktives Exponat eines #ConvolutionalNeuralNetworks zur #Bilderkennung, bei dem alle Zwischenergebnisse der Convolutional, Pool und Fully-Connected-Layer auf eigenen Monitoren dargestellt sind. Mit einer Kamera kann man beliebige (vorhandene) Objekte und Bild-Karten aufnehmen und sich das Klassifikationsergebnis inklusive Konfidenz ansehen. Sehr interessant.
#convolutionalneuralnetworks #bilderkennung #deutschesmuseum #bonn