All Questions
117 questions
97
votes
3
answers
87k
views
What is self-supervised learning in machine learning?
What is self-supervised learning in machine learning? How is it different from supervised learning?
- 41.4k
10
votes
1
answer
1k
views
Can supervised learning be recast as reinforcement learning problem?
Let's assume that there is a sequence of pairs $(x_i, y_i), (x_{i+1}, y_{i+1}), \dots$ of observations and corresponding labels. Let's also assume that the $x$ is considered as independent variable ...
- 873
92
votes
6
answers
103k
views
What's the difference between model-free and model-based reinforcement learning?
What's the difference between model-free and model-based reinforcement learning?
It seems to me that any model-free learner, learning through trial and error, could be reframed as model-based. In ...
- 1,021
8
votes
3
answers
17k
views
What is the difference between a stochastic and a deterministic policy?
In reinforcement learning, there are the concepts of stochastic (or probabilistic) and deterministic policies. What is the difference between them?
- 41.4k
5
votes
2
answers
3k
views
Do convolutional neural networks perform convolution or cross-correlation?
Typically, people say that convolutional neural networks (CNN) perform the convolution operation, hence their name. However, some people have also said that a CNN actually performs the cross-...
- 41.4k
5
votes
4
answers
3k
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 ...
- 3,990
104
votes
9
answers
17k
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 ...
- 1,335
5
votes
3
answers
4k
views
What are the differences between a knowledge base and a knowledge graph?
During my readings, I have seen many authors using the two terms interchangeably, i.e. as if they refer to the same thing. However, we all know about Google's first quotation of "knowledge graph&...
- 151
3
votes
1
answer
2k
views
Why do we need convolutional neural networks instead of feed-forward neural networks?
Why do we need convolutional neural networks instead of feed-forward neural networks?
What is the significance of a CNN? Even a feed-forward neural network will able to solve the image classification ...
- 1,763
19
votes
1
answer
52k
views
What is the difference between tree search and graph search?
I have read various answers to this question at different places, but I am still missing something.
What I have understood is that a graph search holds a closed list, with all expanded nodes, so ...
- 423
5
votes
1
answer
14k
views
How do I show that uniform-cost search is a special case of A*?
How do I show that uniform-cost search is a special case of A*? How do I prove this?
- 323
31
votes
2
answers
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?
- 10.5k
19
votes
1
answer
19k
views
How does LSTM in deep reinforcement learning differ from experience replay?
In the paper Deep Recurrent Q-Learning for Partially Observable MDPs, the author processed the Atari game frames with an LSTM layer at the end. My questions are:
How does this method differ from the ...
- 363
23
votes
2
answers
15k
views
What is the difference between reinforcement learning and optimal control?
Coming from a process (optimal) control background, I have begun studying the field of deep reinforcement learning.
Sutton & Barto (2015) state that
particularly important (to the writing of the ...
- 373
20
votes
3
answers
12k
views
How do I choose the best algorithm for a board game like checkers?
How do I choose the best algorithm for a board game like checkers?
So far, I have considered only three algorithms, namely, minimax, alpha-beta pruning, and Monte Carlo tree search (MCTS). Apparently,...
- 487
18
votes
5
answers
7k
views
What exactly are genetic algorithms and what sort of problems are they good for?
I've noticed that a few questions on this site mention genetic algorithms and it made me realize that I don't really know much about those.
I have heard the term before, but it's not something I've ...
15
votes
2
answers
11k
views
What is the difference between artificial intelligence and computational intelligence?
Having analyzed and reviewed a certain amount of articles and questions, apparently, the expression computational intelligence (CI) is not used consistently and it is still unclear the relationship ...
- 1,183
8
votes
1
answer
9k
views
What is the difference between a stationary and a non-stationary policy?
In reinforcement learning, there are deterministic and non-deterministic (or stochastic) policies, but there are also stationary and non-stationary policies.
What is the difference between a ...
- 41.4k
4
votes
4
answers
5k
views
What is the difference between training and testing in reinforcement learning?
In reinforcement learning (RL), what is the difference between training and testing an algorithm/agent? If I understood correctly, testing is also referred to as evaluation.
As I see it, both imply ...
- 249
4
votes
1
answer
776
views
Is there any difference between a control and an action in reinforcement learning?
There are reinforcement learning papers (e.g. Metacontrol for Adaptive Imagination-Based Optimization) that use (apparently, interchangeably) the term control or action to refer to the effect of the ...
- 41.4k
1
vote
1
answer
385
views
What is meant by "two action selections" in SARSA?
I have some difficulties understanding the difference between Q-learning and SARSA. Here (What are the differences between SARSA and Q-learning?) the following updating formulas are given:
Q-Learning
$...
- 266
58
votes
11
answers
12k
views
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 ...
- 564
50
votes
3
answers
24k
views
What is the difference between strong-AI and weak-AI?
I've heard the terms strong-AI and weak-AI used. Are these well defined terms or subjective ones? How are they generally defined?
- 2,523
47
votes
2
answers
26k
views
What is the relation between Q-learning and policy gradients methods?
As far as I understand, Q-learning and policy gradients (PG) are the two major approaches used to solve RL problems. While Q-learning aims to predict the reward of a certain action taken in a certain ...
- 595
43
votes
5
answers
82k
views
What is the difference between a convolutional neural network and a regular neural network?
I've seen these terms thrown around this site a lot, specifically in the tags convolutional-neural-networks and neural-networks.
I know that a neural network is a system based loosely on the human ...
- 2,925
35
votes
6
answers
28k
views
Is it possible to train the neural network to solve math equations?
I'm aware that neural networks are probably not designed to do that, however asking hypothetically, is it possible to train the deep neural network (or similar) to solve math equations?
So given the ...
- 10.5k
24
votes
2
answers
24k
views
What is the difference between First-Visit Monte-Carlo and Every-Visit Monte-Carlo Policy Evaluation?
I came across these 2 algorithms, but I cannot understand the difference between these 2, both in terms of implementation as well as intuitionally.
So, what difference does the second point in both ...
user9947
23
votes
5
answers
4k
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?
- 333
20
votes
3
answers
4k
views
How are Artificial Neural Networks and the Biological Neural Networks similar and different?
I've heard multiple times that "Neural Networks are the best approximation we have to model the human brain", and I think it is commonly known that Neural Networks are modelled after our brain.
I ...
17
votes
3
answers
9k
views
Are there any applications of reinforcement learning other than games?
Is there a way to teach reinforcement learning in applications other than games?
The only examples I can find on the Internet are of game agents. I understand that VNC's control the input to the ...
13
votes
5
answers
8k
views
What is the fundamental difference between CNN and RNN?
What is the fundamental difference between convolutional neural networks and recurrent neural networks? Where are they applied?
- 151
13
votes
1
answer
5k
views
What is the difference between one-shot learning, transfer learning and fine tuning?
Lately, there are lots of posts on one-shot learning. I tried to figure out what it is by reading some articles. To me, it looks like similar to transfer learning, in which we can use pre-trained ...
- 789
13
votes
3
answers
3k
views
What is a recurrent neural network?
Surprisingly, this wasn't asked before - at least I didn't find anything besides some vaguely related questions.
So, what is a recurrent neural network, and what are their advantages over regular (or ...
- 759
12
votes
1
answer
893
views
What are hyper-heuristics, and how are they different from meta-heuristics?
I wanted to know what the differences between hyper-heuristics and meta-heuristics are, and what their main applications are. Which problems are suited to be solved by hyper-heuristics?
- 399
10
votes
3
answers
8k
views
What is the intuition behind TD($\lambda$)?
I'd like to better understand temporal-difference learning. In particular, I'm wondering if it is prudent to think about TD($\lambda$) as a type of "truncated" Monte Carlo learning?
- 155
9
votes
5
answers
13k
views
Why is the variational auto-encoder's output blurred, while GANs output is crisp and has sharp edges?
I observed in several papers that the variational autoencoder's output is blurred, while GANs output is crisp and has sharp edges.
Can someone please give some intuition why that is the case? I did ...
- 269
9
votes
2
answers
14k
views
What is the difference between an agent function and an agent program?
In section 2.4 (p. 46) of the book Artificial Intelligence: A modern approach (3rd edition), Russell and Norvig write
The job of AI is to design an agent program that implements the agent function — ...
- 437
8
votes
1
answer
31k
views
What is the difference between LSTM and RNN?
What is the difference between LSTM and RNN? I know that RNN is a layer used in neural networks, but what exactly is an LSTM? Is it also a layer with the same characteristics?
- 83
8
votes
6
answers
13k
views
What is the difference between artificial intelligence and robots?
What is the difference between artificial intelligence and robots?
- 1,082
6
votes
3
answers
2k
views
Why are traditional ML models still used over deep neural networks?
I'm still on my first steps in the Data Science field. I played with some DL frameworks, like TensorFlow (pure) and Keras (on top) before, and know a little bit of some "classic machine learning" ...
- 845
4
votes
1
answer
237
views
What is the scope of real-world deep learning applications in 2020?
2015 was a milestone year for AI--"deep learning" was validated in a very public way with AlphaGo. However, at the time, the question was raised: "What else is deep learning good for?&...
- 6,219
3
votes
2
answers
861
views
Apart from the state and state-action value functions, what are other examples of value functions used in RL?
In reinforcement learning, we often define two functions, the state-value function
$$V^\pi(s) = \mathbb{E}_{\pi} \left[\sum_{k=0}^{\infty}
\gamma^{k}R_{t+k+1} \Bigg| S_t=s \right]$$
and the state-...
- 41.4k
2
votes
1
answer
825
views
What are the differences between SARSA and Q-learning? [closed]
From Sutton and Barto's book Reinforcement Learning (Adaptive Computation and Machine Learning series), are the following definitions:
To aid my learning of RL and gain an intuition, I'm focusing on ...
- 335
1
vote
1
answer
439
views
Can I always interpret features as random variables in machine learning safely?
Consider the following statements from Chapter 5: Machine Learning Basics from the book titled Deep Learning (by Aaron Courville et al.)
Machine learning tasks are usually described in terms of how ...
- 3,990
29
votes
4
answers
81k
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.
- 509
26
votes
4
answers
10k
views
Can the decoder in a transformer model be parallelized like the encoder?
Can the decoder in a transformer model be parallelized like the encoder?
As far as I understand, the encoder has all the tokens in the sequence to compute the self-attention scores. But for a decoder,...
- 363
19
votes
4
answers
11k
views
What is the difference between actor-critic and advantage actor-critic?
I'm struggling to understand the difference between actor-critic and advantage actor-critic.
At least, I know they are different from asynchronous advantage actor-critic (A3C), as A3C adds an ...
- 1,077
17
votes
2
answers
2k
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 ...
- 293
17
votes
2
answers
7k
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? ...
- 313
16
votes
1
answer
6k
views
What is the difference between a receptive field and a feature map?
In a CNN, the receptive field is the portion of the image used to compute the filter's output. But one filter's output (which is also called a "feature map") is the next filter's input.
What's the ...
- 261