--Activation Function in CNN--
-Image Analysis-
#imageanalysis #machinelearning #clustering #datascientists #kmeans #deeplearning #neuralnetwork #underfitting
-Image Analysis-
#imageanalysis #machinelearning #clustering #datascientists #kmeans #deeplearning #neuralnetwork #underfitting
A gentle overview on the Deep Learning and Machine Learning
The Deep Learning is a subarea of the Machine Learning that makes use of Deep Neural Networks (with many layers) and specific novel algorithms for the pre-processing of data and regularization of the model: word embeddings, dropout, data-augmentation. Deep Learning takes inspiration from Neuroscience since Neural Networks are a model of the neuronal network within the brain. Unlike the biological brain, where any neuron can connect to any other under some physical constraints, Artificial Neural Networks (ANNs) have a finite number of layers, connections, and fixed direction of the data propagation. So far, ANNs have been ignored by the research and business community. The problem is their computational cost.
Between 2006 and 2012 the research group led by Geoffrey Hinton at the University of Toronto finally parallelized the ANNs algorithms to parallel architectures. The main breakthroughs are the increased number of layers, neurons, and model parameters in general (even over than 10 million) allowing to compute massive amounts of data through the system to train it.
https://lnkd.in/dq87iFy
#neuralnetwork
#deeplearning
#machinelearning
✴️ @AI_Python_EN
The Deep Learning is a subarea of the Machine Learning that makes use of Deep Neural Networks (with many layers) and specific novel algorithms for the pre-processing of data and regularization of the model: word embeddings, dropout, data-augmentation. Deep Learning takes inspiration from Neuroscience since Neural Networks are a model of the neuronal network within the brain. Unlike the biological brain, where any neuron can connect to any other under some physical constraints, Artificial Neural Networks (ANNs) have a finite number of layers, connections, and fixed direction of the data propagation. So far, ANNs have been ignored by the research and business community. The problem is their computational cost.
Between 2006 and 2012 the research group led by Geoffrey Hinton at the University of Toronto finally parallelized the ANNs algorithms to parallel architectures. The main breakthroughs are the increased number of layers, neurons, and model parameters in general (even over than 10 million) allowing to compute massive amounts of data through the system to train it.
https://lnkd.in/dq87iFy
#neuralnetwork
#deeplearning
#machinelearning
✴️ @AI_Python_EN
Vanishing/exploring gradients problem is a well often problem especially when training big networks, so visualizing gradients is a must when training neural networks. Here is the small network's on MNIST dataset gradients flow. A detailed article is on the way to explain many things in deep learning.
#machinelearning #deeplearning #artificialintelligence #computervision #neuralnetwork
❇️ @AI_Python_EN
#machinelearning #deeplearning #artificialintelligence #computervision #neuralnetwork
❇️ @AI_Python_EN
#GraphNeuralNetwork s for Natural Language Processing
#neuralnetwork #NLP
https://bit.ly/33oprRc
❇️ @AI_Python_EN
#neuralnetwork #NLP
https://bit.ly/33oprRc
❇️ @AI_Python_EN