Artificial Intelligence Stack Exchange
Artificial Intelligence Stack Exchange

Questions tagged [deep-learning]

For questions related to deep learning, which refers to a subset of machine learning methods based on artificial neural networks (ANNs) with multiple hidden layers. The adjective deep thus refers to the number of layers of the ANNs. The expression deep learning was apparently introduced (although not in the context of machine learning or ANNs) in 1986 by Rina Dechter in the paper "Learning while searching in constraint-satisfaction-problems".

1925 questions
97
votes
5 answers

How can neural networks deal with varying input sizes?

As far as I can tell, neural networks have a fixed number of neurons in the input layer. If neural networks are used in a context like NLP, sentences or blocks of text of varying sizes are fed to a network. How is the varying input size reconciled…
Asciiom
  • 1,121
  • 1
  • 9
  • 10
97
votes
7 answers

Do scientists know what is happening inside artificial neural networks?

Do scientists or research experts know from the kitchen what is happening inside complex "deep" neural network with at least millions of connections firing at an instant? Do they understand the process behind this (e.g. what is happening inside and…
kenorb
  • 10,423
  • 3
  • 43
  • 91
56
votes
11 answers

What are some well-known problems where neural networks don't do very well?

Background: It's well-known that neural networks offer great performance across a large number of tasks, and this is largely a consequence of their universal approximation capabilities. However, in this post I'm curious about the…
ABIM
  • 535
  • 1
  • 6
  • 15
48
votes
19 answers

Can digital computers understand infinity?

As a human being, we can think infinity. In principle, if we have enough resources (time etc.), we can count infinitely many things (including abstract, like numbers, or real). For example, at least, we can take into account integers. We can think,…
verdery
  • 688
  • 1
  • 6
  • 11
40
votes
5 answers

What is the purpose of an activation function in neural networks?

It is said that activation functions in neural networks help introduce non-linearity. What does this mean? What does non-linearity mean in this context? How does the introduction of this non-linearity help? Are there any other purposes of…
Mohsin
  • 972
  • 1
  • 9
  • 15
39
votes
3 answers

Why is Lisp such a good language for AI?

I've heard before from computer scientists and from researchers in the area of AI that that Lisp is a good language for research and development in artificial intelligence. Does this still apply, with the proliferation of neural networks and deep…
Alecto Irene Perez
  • 599
  • 1
  • 6
  • 10
31
votes
3 answers

Can BERT be used for sentence generating tasks?

I am a new learner in NLP. I am interested in the sentence generating task. As far as I am concerned, one state-of-the-art method is the CharRNN, which uses RNN to generate a sequence of words. However, BERT has come out several weeks ago and is…
ch271828n
  • 413
  • 1
  • 4
  • 11
31
votes
5 answers

Is it possible to train a neural network as new classes are given?

I would like to train a neural network (NN) where the output classes are not (all) defined from the start. More and more classes will be introduced later based on incoming data. This means that, every time I introduce a new class, I would need to…
Fr_nkenstien
  • 457
  • 1
  • 4
  • 10
29
votes
2 answers

What are "bottlenecks" in neural networks?

What are "bottlenecks" in the context of neural networks? This term is mentioned, for example, in this TensorFlow article, which also uses the term "bottleneck values". How does one calculate bottleneck values? How do these values help image…
Anurag Singh
  • 401
  • 1
  • 5
  • 11
29
votes
4 answers

Can a neural network be used to predict the next pseudo random number?

Is it possible to feed a neural network the output from a random number generator and expect it learn the hashing (or generator) function, so that it can predict what will be the next generated pseudo-random number? Does something like this already…
AshTyson
  • 401
  • 1
  • 4
  • 4
25
votes
3 answers

How do I handle large images when training a CNN?

Suppose that I have 10K images of sizes $2400 \times 2400$ to train a CNN. How do I handle such large image sizes without downsampling? Here are a few more specific questions. Are there any techniques to handle such large images which are to be…
WaterRocket8236
  • 403
  • 1
  • 4
  • 7
24
votes
3 answers

How to choose an activation function for the hidden layers?

I choose the activation function for the output layer depending on the output that I need and the properties of the activation function that I know. For example, I choose the sigmoid function when I'm dealing with probabilities, a ReLU when I'm…
gvgramazio
  • 696
  • 2
  • 7
  • 19
24
votes
3 answers

Why do most deep learning papers not include an implementation?

I'm a novice researcher, and as I started to read papers in the area of deep learning I noticed that the implementation is normally not added and is needed to be searched elsewhere, and my question is how come that's the case? The paper's authors…
Gilad Deutsch
  • 629
  • 5
  • 12
24
votes
5 answers

What is non-Euclidean data?

What is non-Euclidean data? Here are some sub-questions Where does this type of data arise? I have come across this term in the context of geometric deep learning and graph neural networks. Apparently, graphs and manifolds are non-Euclidean data.…
nbro
  • 39,006
  • 12
  • 98
  • 176
23
votes
5 answers

What is the difference between machine learning and deep learning?

Can someone explain to me the difference between machine learning and deep learning? Is it possible to learn deep learning without knowing machine learning?
Addis
  • 333
  • 5
  • 9
1
2 3
…
99 100