copy and paste this google map to your website or blog!
Press copy button and paste into your blog or website.
(Please switch to 'HTML' mode when posting into your blog. Examples: WordPress Example, Blogger Example)
Input Form:Deal with this potential dealer,buyer,seller,supplier,manufacturer,exporter,importer
(Any information to deal,buy, sell, quote for products or service)
machine learning - What is a fully convolution network? - Artificial . . . Fully convolution networks A fully convolution network (FCN) is a neural network that only performs convolution (and subsampling or upsampling) operations Equivalently, an FCN is a CNN without fully connected layers Convolution neural networks The typical convolution neural network (CNN) is not fully convolutional because it often contains fully connected layers too (which do not perform the
Why is my validation test accuracy higher than my training accuracy Closed 2 years ago Is this due to my dropout layers being disabled during evaluation? I'm classifying the CIFAR-10 dataset with a CNN using the Keras library There are 50000 samples in the training set; I'm using a 20% validation split for my training data (10000:40000) I have 10000 instances in the test set
What are the features get from a feature extraction using a CNN? So, the convolutional layers reduce the input to get only the more relevant features from the image, and then the fully connected layer classify the image using those features, isn't it? I think I've just understood how a CNN works
When training a CNN, what are the hyperparameters to tune first? I am training a convolutional neural network for object detection Apart from the learning rate, what are the other hyperparameters that I should tune? And in what order of importance? Besides, I r
Extract features with CNN and pass as sequence to RNN But if you have separate CNN to extract features, you can extract features for last 5 frames and then pass these features to RNN And then you do CNN part for 6th frame and you pass the features from 2,3,4,5,6 frames to RNN which is better The task I want to do is autonomous driving using sequences of images
deep learning - Artificial Intelligence Stack Exchange I'm looking for a neural network architecture that excels in counting objects For example, CNN that can output the number of balls (or any other object) in a given image I already found articles
Why CNN filters (kernels) are randomly initialized? I learned that when CNN filters are defined, they are initialized with random weights and bias(Im not sure about bias) Then as learning step goes on, the weight values change and each filter makes