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This method would likely rely on a [https*Possible packages for building CNN: TensorFlow, PyTorch, scikit*Current training dataset:<code>The File to Rule Them All<//encode>, contains information of 160 accelerators (homepage url, cohort url etc.wikipedia).org/wiki/Convolutional_neural_network convolutional neural network (We will train our model on those 145 accelerators that have cohort urls found*Type of input for CNN)] to classify HTML elements present in model: picture of the web page screenshots. Implementation could be achieved by combining (generated from the VGG16 model or ResNet architecture with batch normalization to increase accuracy in this context.above screenshot tool) and cohort indicator (1 - it is a cohort page, 0 - not a cohort page)
→Image Processing
===Image Processing===
This method would likely rely on a [https://en.wikipedia.org/wiki/Convolutional_neural_network convolutional neural network (CNN)] to classify HTML elements present in web page screenshots. Implementation could be achieved by combining the VGG16 model or ResNet architecture with batch normalization to increase accuracy in this context.
