I want to use deep learning in my project. I went through a couple of papers and a question occurred to me: is there any difference between convolution neural network and deep learning? Are these things the same or do they have any major differences, and which is better?
Solved – the difference between convolutional neural networks and deep learning
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Best Answer
Deep Learning is the branch of Machine Learning based on Deep Neural Networks (DNNs), meaning neural networks with at the very least 3 or 4 layers (including the input and output layers). But for some people (especially non-technical), any neural net qualifies as Deep Learning, regardless of its depth. And others consider a 10-layer neural net as shallow.
Convolutional Neural Networks (CNNs) are one of the most popular neural network architectures. They are extremely successful at image processing, but also for many other tasks (such as speech recognition, natural language processing, and more). The state of the art CNNs are pretty deep (dozens of layers at least), so they are part of Deep Learning. But you can build a shallow CNN for a simple task, in which case it's not (really) Deep Learning.
But CNNs are not alone, there are many other neural network architectures out there, including Recurrent Neural Networks (RNN), Autoencoders, Transformers, Deep Belief Nets (DBN = a stack of Restricted Boltzmann Machines, RBM), and more. They can be shallow or deep. Note: even shallow RNNs can be considered part of Deep Learning since training them requires unrolling them through time, resulting in a deep net.