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Results tagged with machine-learning
Search options not deleted user 76883
Machine Learning is a subfield of computer science that draws on elements from algorithmic analysis, computational statistics, mathematics, optimization, etc. It is mainly concerned with the use of data to construct models that have high predictive/forecasting ability. Topics include modeling building, applications, theory, etc.
1 vote
Accepted
Normal equation for linear regression
It is basically a matter of convention, which becomes a bit more clear if you write the whole thing in terms of elements, rather than vectors. Consider $$ \theta^T \theta = \sum_{n=1}^N \theta_n \the …
- 1,585
2 votes
Accepted
How to comptute principal component from three points in two dimensional space?
I think you may have forgotten to subtract the mean. As far as I know you have to center the data, otherwise you will compute variance with respect to the origin, rather than the variance within the d …
- 1,585
0 votes
Low accuracy on MNIST Dataset
I'm fairly certain there is something wrong with the way you load the data. I modified the code like this: import numpy as np from keras.datasets import mnist (x_train, y_train), (x_test, y_test) …
- 1,585
1 vote
Why do we operate with graphical models in VAE, if there are no probabilites involved?
Not sure which type of code you were looking at but e.g. here they do sampling of random variables. The encoder deterministically maps the input X to mean and standard deviation vectors. Using these v …
- 1,585
3 votes
Accepted
Convolution and Pooling as Infinitely strong priors
A prior distribution expresses your assumptions about the model without observing any data. E.g. when doing linear regression, you a priori assume that the slope is close to zero. Now you start measur …
- 1,585
0 votes
Clause type classification
Not sure where you are in terms of prior knowledge, but this blog post might get you started.
- 1,585
1 vote
Finding similarity between two datasets
I would say that they are probability distributions. You can interpret them as the probability that a randomly drawn person from a given county belongs to the white majority. However, they are not pro …
- 1,585
2 votes
Accepted
Backpropagation and Stochastic Gradient Descent(SGD)
Stochastic Gradient Descent (SGD) is an optimization method. As the name suggests, it depends on the gradient of the optimization objective. Let's say you want to train a neural network. Usually, the …
- 1,585
1 vote
Convolutional neural network giving high confidence on wrong classification
Since your model seems to be doing fine on the correct hand signs, I don't think there is anything wrong. It's a common misconception that you can interpret the model output as a confidence measure. C …
- 1,585
1 vote
VAE generates bad images. due to unbalanced loss functions?
The issue is in your sampling procedure. The purpose of a VAE is to train a neural network, the decoder, that takes samples $z$ from a normal distribution $p(z)$ and maps them to images $x$ such that …
- 1,585
5 votes
Accepted
PCA for complex-valued data
Apparently this functionality is left out intentionally, see here. I'm afraid you have to use SVD, but that should be fairly straightforward: def pca(X): mean = X.mean(axis=0) center = X - m …
- 1,585