Is a neural network essential for deep learning? I received preliminary materials on deep learning in my class. It was written as follows. This raised me the question of the basic meaning of the word deep learning.

Deep learning is a machine learning method using a multi-layer neural network.


*

*Is a neural network essential for deep learning?

*Isn't it possible to do deep learning without a neural network by using PCA? (Example: PCANet)

I'm confused by similar terms like deep learning and deep neural networks. I have a month to start school and I can't contact my teacher during that time. I would appreciate it if you could tell me.
 A: This answer depends on the definition of artificial neural network (ANN) you take to be true.
See my question here: What *is* an Artificial Neural Network?.
Therefore, no objective answer can be given since:

*

*To accommodate all definitions of ANNs, they are simply defined as arbitrary computational graphs, with tunable parameters (even if they are not tuned, random neural networks are still ANNs)

*Differentiability is not required (for example, genetic algorithms and other metaheuristics have been used to learn ANNs)

I will however, propose a counterfactual approach here that, in my experience, at least makes some people to conclude that deep learning is not solely based on ANNs:

*

*Do you consider hierarchical hidden markov models (HHMM)/hierarchical Bayesian networks deep learning?

*Do you consider them to be neural networks?

If you answered 'yes' and 'no', then the overall answer is definitely 'no', for you there is a deep learning model that is not a neural network.
Other combinations of answer will lead to undefined overall answers.
A: Deep learning is machine learning done using “deep” neural networks, i.e. such that have multiple (>2) layers. So you cannot do it without neural networks. For using other kinds of machine learning just use “machine learning” term, that includes neural networks as well.
A: This is a good question.

Is a neural network essential for deep learning?

Yes, your teacher provided you with a correct definition of deep learning. You can still do machine learning (a broader category) without neural networks, but you need a neural network for it to qualify as 'deep learning'.

Isn't it possible to do deep learning without a neural network by using PCA? (Example: PCANet)

Based on the answer to the last part, no. It by definition wouldn't be 'deep' anymore.
PCANet is actually a neural network, by the way.
PCA, on the other hand, isn't 'deep'. If you stack several layers of PCA on top of each other, then there is an equivalent single-layer PCA you could have done, because composing those linear transforms will just give you another linear transform.
A: I'm going to disagree with the other answers. Fundamentally, I would say that deep learning is defined by a hierarchy of learned representations, and not by which particular model is used to define these representations. Indeed, this is how Goodfellow et al define it in the introductory section of their text Deep Learning (neural networks are not mentioned until later).
In other words, the key with deep learning is that we are effectively learning a series of transformations of our data. Typically, these transformations define a neural network, with the activations of each layer serving as the transformed input data. However, this need not be the case. Deep Gaussian processes, for instance, have been gaining some attention in the research community.
However, if I were teaching an introductory class, I would feel perfectly comfortable using your professor's definition: in practice, people overwhelmingly use neural networks for deep learning.
You can do deep learning with basically any nonlinear model. PCA, being linear, does not qualify (though nonlinear analogues of PCA, such as kernel PCA, can qualify, see this article).
A: To answer from a different perspective, one philosophical point may help: the concept of learning in deep learning.
In deep learning, there are multiple learning steps. In the first step, the data input is 'converted' (or learned) into a synthetic intermediate output (a bit higher abstraction, loosely speaking). Then in each step, the previous output is progressively learned (or 'transformed') into higher abstraction features, which may or may not be comprehensible to humans. Loosely speaking, the combination of these layers will approximate the 'model' for you, we don't need to specify any model or hypothesis beforehand. This is the same 'learning' concept as in cognitive science: find/construct higher abstraction from raw input.
So, do we need NN for deep learning? Yes, in practice. With my limited knowledge, I would say this is the most convenient and efficient way to do it. In theory, it depends. If you build a 'learning' framework in which your model can create 'deep' abstraction (iteratively increasing) from raw input to achieve the task at hand, it may count as deep learning.
In practice, it's a bit simplistic to view Deep Learning as just a vanilla multi-layer neural network. More than often, the workflow includes different NN-components and other transformation layers, each part can be wildly different from the next.
I hope this answer provide some useful insights without the need to use technical terms.
A: Deep learning is a subset of machine learning, which is a field dedicated to the study and development of machines that can learn, and the goal is of deep learning  is to achieve eventually attain general artificial intelligence. Neural network is just one of the  biological inspired model.
In simple terms I would define deep learning a subset method in machine learning primarily using artificial neural networks which are inspired by human brain. See the following  diagram.
Classical machine learning uses classical statistics/mathematical modelling  while deep learning  use biological models aka neural networks.
