# Tag Info

Accepted

### Why should the number of neurons in a hidden layer be a power of 2?

I have read somewhere on the web (I lost the reference) that the number of units (or neurons) in a hidden layer should be a power of 2 because it helps the learning algorithm to converge faster. I ...
• 24k
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### What are the implications of the "No Free Lunch" theorem for machine learning?

This is a really common reaction after first encountering the No Free Lunch theorems (NFLs). The one for machine learning is especially unintuitive, because it flies in the face of everything that's ...
• 8,877

### Can artificial intelligence be thought of as optimization?

A good answer to this question depends on what you want to use the labels for. When I think about "optimization," I think about a solution space and a cost function; that is, there are many possible ...
• 4,182
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### Loss jumps abruptly when I decay the learning rate with Adam optimizer in PyTorch

I see no reason why decaying learning rates should create the kinds of jumps in losses that you are observing. It should "slow down" how quickly you "move", which in the case of a loss that otherwise ...
• 9,379
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### What are hyper-heuristics, and how are they different from meta-heuristics?

TL:DR: Hyper-heuristics are metaheuristics, suited for solving the same kind of optimization problems, but (in principle) affording a "rapid prototyping" approach for non-expert practitioners. In ...
• 7,046
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### How to avoid falling into the "local minima" trap?

There are several elementary techniques to try and move a search out of the basin of attraction of local optima. They include: Probabalistically accepting worse solutions in the hope that this will ...
• 7,046
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### What is the difference between reinforcement learning and evolutionary algorithms?

Evolutionary algorithms (EAs) are a family of algorithms inspired by the biological evolution that can be used to solve (constrained or not) optimization problems where the function that needs to be ...
• 33.8k
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### Why is the learning rate generally beneath 1?

If the learning rate is greater than or equal to $1$ the Robbins-Monro condition $$\sum _{{t=0}}^{{\infty }}a_{t}^{2}<\infty\label{1}\tag{1},$$ where $a_t$ is the learning rate at iteration $t$, ...
• 33.8k

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

You can run gradient descent without back propagation, in some cases: Simple structures such as linear or logistic regression, where the gradients can be calculated directly from the inputs and cost ...
• 24k
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### Why is the perceptron criterion function differentiable?

$\max(-y_i(w x_i), 0)$ is not partial derivable respect $w$ if $w x_i=0$. Loss functions are problematic when not derivable in some point, but even more when they are flat (constant) in some interval ...
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### If Deep Learning is non convex, then why do use a convex loss function?

Well, you are definitely mixing two different things. Here are those bits: The function that deep learning approximates is basically a function that best fits the INPUT DATA points. You should not ...
• 186
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### What are the limitations of the hill climbing algorithm and how to overcome them?

As @nbro has already said that Hill Climbing is a family of local search algorithms. So, when you said Hill Climbing in the question I have assumed you are talking about the standard hill climbing. ...
• 1,953
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$g(x) = x^2$ is indeed a parabola and thus has just one optimum. However, the $\text{MSE}(\boldsymbol{x}, \boldsymbol{y}) = \sum_i (y_i - f(x_i))^2$, where $\boldsymbol{x}$ are the inputs, $\... • 33.8k 5 votes ### Can artificial intelligence be thought of as optimization? I can offer two (at first sight, conflicting) perspectives on this: Firstly: If the letter string 'abc' becomes 'abd' what would "doing the same thing" to 'ijk' look like? This is just one example ... • 7,046 5 votes ### What is the actual learning algorithm: back-propagation or gradient descent? Gradient descent (GD) is an optimisation algorithm, that is, it is used to find a (local) minimum of a multi-variable and differentiable function$f$. GD is an iterative and numerical optimisation ... • 33.8k 5 votes ### What are the limitations of the hill climbing algorithm and how to overcome them? Hill climbing is not an algorithm, but a family of "local search" algorithms. Specific algorithms which fall into the category of "hill climbing" algorithms are 2-opt, 3-opt, 2.5-opt, 4-opt, or, in ... • 33.8k 5 votes Accepted ### When should we use algorithms like Adam as opposed to SGD? Empirically, I observed that algorithms like Adam and RMSProp tended to give me a final higher performance (in my case, the accuracy) on (the validation dataset) with respect to SGD. However, I also ... • 33.8k 4 votes ### What are the techniques for detecting and preventing overfitting? Usually you keep track of training loss and validation loss and apply proper regularization technique (such as L1, L2, dropout, DropConnect, etc.). The more interesting technique is to observe your ... • 341 4 votes Accepted ### What AI technique should I use to assign a person to a task? What you have could be well described as a Task Allocation problem, which is studied as part of the planning subfield of AI. Chapters 10 & 11 of Russell & Norvig provide a good overview of ... • 8,877 4 votes Accepted ### What is an objective function? The "objective function" is the function that you want to minimise or maximise in your problem. The expression "objective function" is used in several different contexts (e.g. machine learning or ... • 33.8k 4 votes Accepted ### Could error surface shape be useful to detect which local minima is better for generalization? In general I agree with @nbro answer, nevertheless sticking strictly to this specific question I'd like to share some speculations: what the author of the question provides us with is the Loss ... 4 votes ### In deep learning, is it possible to use discontinuous activation functions? Even the first artificial neural network - Rosenblatt's perceptron [1] had a discontinuous activation function. That network is in introductory chapters of many textbooks about AI. For example, ... 4 votes Accepted ### How are these equations of SGD with momentum equivalent? The first two equations are equivalent. The last equation can be equivalent if you scale$\alpha$appropriately. Equation 1 Consider the equation from the Stanford slide:$\$ v_{t}=\rho v_{t-1}+\nabla ...
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When dealing with optimization problems, a fundamental distinction is whether the objective is a (deterministic) function, or an expectation of some function. I will refer to these cases as the ...
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### Is a calculus or ML approach to varying learning rate as a function of loss and epoch been investigated?

Has this been done? Difficult to prove a negative, but I suspect although plenty of research has been done into finding ideal learning rate values (the need for learning rate at all is an annoyance), ...
• 24k
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### Can I compute the fitness of an agent based on a low number of runs of the game?

You can probably get away with a relatively low X for two reasons: The Central Limit Theorem. This tells us that the accuracy in the estimate of an agent's fitness will improve as the square root of ...
• 8,877
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### Maximizing or Minimizing in Trust Region Policy Optimization?

The differences you have observed between the two different versions of the TRPO paper are due to different formalizations of the problem and the objective. In the first version of the paper you ...
• 9,379
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### Is there a way to define the boundaries of the optimal size of a training set?

For a finite value to be 'optimal,' typically you need some benefit from more paired up with some cost for more, and eventually the lines cross because the benefit decreases and the cost increases. ...
• 4,182