Questions tagged [machine-learning]

For questions related to machine learning (ML), which is a set of methods that can automatically detect patterns in data, and then use the uncovered patterns to predict future data, or to perform other kinds of decision making under uncertainty (such as planning how to collect more data). ML is usually divided into supervised, unsupervised and reinforcement learning. Deep learning is a subfield of ML that uses deep artificial neural networks.

Filter by
Sorted by
Tagged with
80
votes
3answers
72k views

What is self-supervised learning in machine learning?

What is self-supervised learning in machine learning? How is it different from supervised learning?
55
votes
4answers
14k views

Are neural networks prone to catastrophic forgetting?

Imagine you show a neural network a picture of a lion 100 times and label it with "dangerous", so it learns that lions are dangerous. Now imagine that previously you have shown it millions ...
33
votes
4answers
34k views

What is the time complexity for training a neural network using back-propagation?

Suppose that a NN contains $n$ hidden layers, $m$ training examples, $x$ features, and $n_i$ nodes in each layer. What is the time complexity to train this NN using back-propagation? I have a basic ...
3
votes
2answers
435 views

What is machine learning?

What is the definition of machine learning? What are the advantages of machine learning?
6
votes
1answer
2k views

How can the convolution operation be implemented as a matrix multiplication?

How can the convolution operation used by CNNs be implemented as a matrix-vector multiplication? We often think of the convolution operation in CNNs as a kernel that slides across the input. However, ...
91
votes
10answers
15k views

What is the difference between artificial intelligence and machine learning?

These two terms seem to be related, especially in their application in computer science and software engineering. Is one a subset of another? Is one a tool used to build a system for the other? What ...
11
votes
4answers
3k views

What are the purposes of autoencoders?

Autoencoders are neural networks that learn a compressed representation of the input in order to later reconstruct it, so they can be used for dimensionality reduction. They are composed of an encoder ...
3
votes
4answers
727 views

What is the fundamental difference between an ML model and a function?

A model can be roughly defined as any design that is able to solve an ML task. Examples of models are the neural network, decision tree, Markov network, etc. A function can be defined as a set of ...
77
votes
3answers
55k views

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 ...
34
votes
3answers
26k views

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, ...
30
votes
7answers
5k views

Is artificial intelligence vulnerable to hacking? [closed]

The paper The Limitations of Deep Learning in Adversarial Settings explores how neural networks might be corrupted by an attacker who can manipulate the data set that the neural network trains with. ...
31
votes
2answers
1k views

How is a deep neural network different from other neural networks?

How is a neural network having the "deep" adjective actually distinguished from other similar networks?
8
votes
1answer
10k views

What is a fully convolution network?

I was surveying some literature related to Fully Convolutional Networks and came across the following phrase, A fully convolutional network is achieved by replacing the parameter-rich fully ...
20
votes
5answers
8k views

What is the difference between latent and embedding spaces?

In general, the word "latent" means "hidden" and "to embed" means "to incorporate". In machine learning, the expressions "hidden (or latent) space" ...
5
votes
2answers
4k views

Are neural networks statistical models?

By reading the abstract of Neural Networks and Statistical Models paper it would seem that ANNs are statistical models. In contrast Machine Learning is not just glorified Statistics. I am looking ...
13
votes
2answers
5k views

Why did machine learning only become viable after Nvidia's chips were available?

I listened to a talk by a panel consisting of two influential Chinese scientists: Wang Gang and Yu Kai and others. When being asked about the biggest bottleneck of the development of artificial ...
8
votes
1answer
435 views

What are the different approaches used in Machine Learning?

There seem to be so many sub-fields, so I'm interested in getting a better understanding of the approaches. I'm looking for information on a single framework per answer, in order to allow for ...
2
votes
1answer
362 views

How can supervised learning be viewed as a conditional probability of the labels given the inputs?

In the literature and textbooks, one often sees supervised learning expressed as a conditional probability, e.g., $$\rho(\vec{y}|\vec{x},\vec{\theta})$$ where $\vec{\theta}$ denotes a learned set of ...
4
votes
2answers
446 views

How can I handle overfitting in reinforcement learning problems?

So this is my current result (loss and score per episode) of my RL model in a simple two players game: I use DQN with CNN as a policy and target networks. I train my model using Adam optimizer and ...
5
votes
3answers
1k views

What is the actual learning algorithm: back-propagation or gradient descent?

What is the actual learning algorithm: back-propagation or gradient descent (or, in general, the optimization algorithm)? I am reading through chapter 8 of Parallel Distributed Processing hand book ...
3
votes
2answers
782 views

What is a probability distribution in machine learning?

If we were learning or working in the machine learning field, then we frequently come across the term "probability distribution". I know what probability, conditional probability, and ...
52
votes
4answers
86k views

How to select number of hidden layers and number of memory cells in an LSTM?

I am trying to find some existing research on how to select the number of hidden layers and the size of these of an LSTM-based RNN. Is there an article where this problem is being investigated, i.e., ...
60
votes
10answers
41k views

Why is Python such a popular language in the AI field?

First of all, I'm a beginner studying AI and this is not an opinion-oriented question or one to compare programming languages. I'm not implying that Python is the best language. But the fact is that ...
26
votes
4answers
31k views

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-...
15
votes
3answers
741 views

How would an AI learn language?

I was think about AIs and how they would work, when I realised that I couldn't think of a way that an AI could be taught language. A child tends to learn language through associations of language and ...
20
votes
5answers
3k views

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?
15
votes
3answers
36k views

How do I choose the optimal batch size?

Batch size is a term used in machine learning and refers to the number of training examples utilised in one iteration. The batch size can be one of three options: batch mode: where the batch size is ...
10
votes
1answer
869 views

What are the implications of the "No Free Lunch" theorem for machine learning?

The No Free Lunch (NFL) theorem states (see the paper Coevolutionary Free Lunches by David H. Wolpert and William G. Macready) any two algorithms are equivalent when their performance is averaged ...
14
votes
3answers
351 views

Why does Batch Normalization work?

Adding BatchNorm layers improves training time and makes the whole deep model more stable. That's an experimental fact that is widely used in machine learning practice. My question is - why does it ...
6
votes
2answers
2k views

What are other examples of theoretical machine learning books?

I am looking for a book about machine learning that would suit my physics background. I am more or less familiar with classical and complex analysis, theory of probability, сcalculus of variations, ...
12
votes
1answer
2k views

How exactly can ReLUs approximate non-linear and curved functions?

Currently, the most commonly used activation functions are ReLUs. So I answered this question What is the purpose of an activation function in neural networks? and, while writing the answer, it struck ...
4
votes
2answers
198 views

Mathematical foundations of the ability to learn

I am an undergraduate student in applied mathematics with an interest in artificial intelligence. I am currently exploring topics where I could do research. Coming from a mathematical background I am ...
11
votes
3answers
493 views

What are the main problems hindering current AI development?

I have a background in Computer Engineering and have been working on developing better algorithms to mimic human thought. (One of my favorites is Analogical Modeling as applied to language processing ...
7
votes
2answers
286 views

Why is creating an AI that can code a hard task?

For people who have experience in the field, why is creating AI that has the ability to write programs (that are syntactically correct and useful) a hard task? What are the barriers/problems we have ...
7
votes
5answers
2k views

How can action recognition be achieved?

For example, I would like to train my neural network to recognize the type of actions (e.g. in commercial movies or some real-life videos), so I can "ask" my network in which video or movie (and at ...
7
votes
3answers
2k views

What is the difference between hypothesis space and representational capacity?

I am reading Goodfellow et al Deeplearning Book. I found it difficult to understand the difference between the definition of the hypothesis space and representation capacity of a model. In Chapter 5,...
11
votes
3answers
290 views

What is a deep neural network? [duplicate]

What is the definition of a deep neural network? Why are they so popular or important?
5
votes
1answer
403 views

Which functions can be activation functions?

What are the required characteristics of an activation function (in a neural network)? Which functions can be activation functions? For example, which of the functions below can be used as an ...
3
votes
1answer
554 views

How does an unsupervised learning model learn?

How does an unsupervised learning model learn, if it does not involve any target values?
2
votes
2answers
90 views

AI with conflicting objectives?

A recent question on AI and acting recalled me to the idea that in drama, there are not only conflicting motives between agents (characters), but a character may themselves have objectives that are in ...
14
votes
1answer
619 views

How to stay a up-to-date researcher in ML/RL community?

As a student who wants to work on machine learning, I would like to know how it is possible to start my studies and how to follow it to stay up-to-date. For example, I am willing to work on RL and MAB ...
12
votes
1answer
1k views

What are all the different kinds of neural networks used for? [closed]

I found the following neural network cheat sheet (Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data). What are all these different kinds of neural networks used ...
23
votes
4answers
58k views

Why does C++ seem less widely used than Python in AI?

I just want to know why do machine learning engineers and AI programmers use languages like Python to perform AI tasks and not C++, even though C++ is technically a more powerful language than Python.
19
votes
2answers
3k views

What is the "Hello World" problem of Reinforcement Learning?

As we all know, "Hello World" is usually the first program that any programmer learns/implements in any language/framework. As Aurélien Géron mentioned in his book that MNIST is often called ...
15
votes
2answers
1k views

When is deep learning overkill?

For example, for classifying emails as spam, is it worthwhile - from a time/accuracy perspective - to apply deep learning (if possible) instead of another machine learning algorithm? Will deep ...
7
votes
3answers
5k views

What are the differences between transfer learning and meta learning?

What are the differences between meta-learning and transfer learning? I have read 2 articles on Quora and TowardDataScience. Meta learning is a part of machine learning theory in which some ...
12
votes
3answers
20k views

Is it possible to train a neural network to estimate a vehicle's length?

I have a large dataset (over 100k samples) of vehicles with the ground truth of their lengths. Is it possible to train a deep network to measure/estimate vehicle length? I haven't seen any papers ...
5
votes
1answer
234 views

How do recommendation systems work?

How do recommendation systems (e.g. on Youtube) work? Apparently, every user gets different recommendations depending on his location, his past liked videos, etc. So it would seem like a training ...
5
votes
1answer
577 views

How can I apply reinforcement learning to solve this asteroid game?

Introduction An attractive asteroid game was described in the paper Learning Policies for Embodied Virtual Agents through Demonstration (2017, Jonathan Dinerstein et al.): In our first experiment, ...
13
votes
2answers
3k views

What is the difference between active learning and online learning?

The definitions for these two appear to be very similar, and frankly, I've been only using the term "active learning" the past couple of years. What is the actual difference between the two? ...