# Tag Info

Accepted

### Use of machine learning in computational fluid dynamics

It's a long-running joke that CFD stands for "colorful fluid dynamics". Nevertheless, it is used -- and useful -- in a wide range of applications. I believe your discontent stems from not ...
Accepted

### Sensitivity of $y$ w.r.t. to $x$ in $y=f(x)$ where f is a routine

The act of using automatic differentiation for derivative calculations of general programs has become known as differentiable programming. There are many grades of differentiable programming across ...
Accepted

### Why aren't Krylov subspace methods popular in the Machine Learning community compared to Gradient Descent?

On a basic level, I don't buy the argument that you have to "solve a linear system for many machine learning algorithms". Much more, you usually have to optimize a non-linear equation which ...
• 2,862

### Faster Logistic Function

Yes! There are nice approximations of the logistic. Plot of Approximating Functions As shown below, several functions approximate the logistic (shown as blue dots). This graph is available ...
• 3,566
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### Markov (Chain) image generators?

I've implemented this recently, basically it counts how many times each specific colour borders another colour to make up a frequency table. To generate an image, a random colour and position are ...
• 226

### Are there tasks in machine learning which require double precision floating points?

I am not an expert in machine learning, but I can outline the considerations that are relevant. The numerical calculations in machine learning are generally linear algebra -- either solving linear ...
• 16.3k

### Use of machine learning in computational fluid dynamics

I think you are mixing a couple different ideas that are causing confusion. Yes, there are a wide variety of ways to discretize a given problem. Choosing an appropriate way may look like "voodoo" ...
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### Seeking a free symbolic regression software

I wrote a Python package called PyPGE. PyPGE is a Symbolic Regression implementation based on Prioritized Grammar Enumeration (1), not Evolutionary or Genetic Programming. It produces a deterministic ...
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### Advantage of diagonal "jitter" for numerical stability?

Look up something on Tikhonov regularization, also known as ridge regression in machine learning. This is a standard technique (but I agree that the explanation in that notebook is somewhat poor). ...
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### Learning computational science through guided discovery

I learn computation science through Practical Numerical Methods with Python. https://github.com/numerical-mooc/numerical-mooc/wiki It covers finite differencing and many other numerical algorithms. ...

### Seeking a free symbolic regression software

I found the gramEvol R package flexible and easy to use. They have a small tutorial in which they rederive Kepler's third law from data. Note that it relies on Genetic Programmic for its optimisation ...

### Interpolation of Data Value using Optimized Weighting of Its Features

The problem is not convex, as one can verify using the simplest possible choice: $N=2, d=1$ and with data $x_1=x_2=1, f_1=1, f_2=0$. In that case, your objective function is  F(C) = 2\left(1-\exp(-...
• 52.2k
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### Why is FLOP(Floating Point Operations Per Second) mentioned as a specification on every GPU?

This metric is pretty much as misleading (or useful, depending on your perspective) for GPUs as it is for CPUs. Currently, a lot of applications/algorithm's implementations are limited more by memory ...
• 8,521
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### Interpolation vs. Neural network

I have limited experience in machine learning; however, simplifying, you can think of it as a "trained black box". I would say, you have to know a lot about your problem, the behaviour of your ...
• 8,521

### How to filter customer voice from customer - agent conversation recordings?

This seems to be the "Cocktail Party Problem". Andrew Ng's machine learning course on Coursera gives a solution based on SVD for this problem. See the first week's course notes. Ng refers to ...
• 609
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### Does the loss function in a deep neural network act as a norm?

Connection between Mean squared error and $L_2$ norm: First the $L_2$ norm of a vector $\boldsymbol{x}\in\mathbb{R}^n$ is defined as \begin{align*} \lVert \boldsymbol{x}\rVert_2=\left(\sum\limits_{i=1}...
• 439
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### What problems does softmax() solve and when should I think of using it - in simple terms

Suppose you are training a neural network to predict the probability that a given picture is a picture of a cat, dog or tiger. This is an example of a problem called multi-class classification. The ...
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