Can I use CNN for non-image data?

Despite its huge success with image data CNN is not designed to handle non-image. (and non-time series) data. Arguably, any problem that can represent the correlation of features of a given data example in a single map, may be attempted via CNN.

Can CNN be used for non-image and text data?

A lot of data such as genomic, transcriptomic, methylation, mutation, text, spoken words, financial and banking are in non-image form and ML techniques are dominantly used in these fields. Moreover, CNN can’t be used because it requires an image as an input.

How much data does it take to train CNN?

Generally speaking, you need thousands, but usually, orders of magnitude more. There are smaller examples, e.g. the LUNA16 lung nodule detection challenge only has around 1000 images..

Can deep learning be used for non-image data?

Yes you can use deep learning techniques to process non-image data.

Can deep learning be used for non image data?

How many photos do I need to train CNN?

Usually around 100 images are sufficient to train a class. If the images in a class are very similar, fewer images might be sufficient. the training images are representative of the variation typically found within the class.

How many images do I need to train AI?

Computer Vision: For image classification using deep learning, a rule of thumb is 1,000 images per class, where this number can go down significantly if one uses pre-trained models [6].

What is a 1D CNN?

Summary. In 1D CNN, kernel moves in 1 direction. Input and output data of 1D CNN is 2 dimensional. Mostly used on Time-Series data. In 2D CNN, kernel moves in 2 directions.

Is deep learning used only for images?

No, absolutely not. There are three major categories of deep learning. MLP (Multilayer perceptron or Feed-forward network): It does not have memory and does not use spatial filter or pooling or any convolution layer.