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Results tagged with machine-learning
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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.
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Why is Standard Deviation based on L2 Variance and not L1 Variance
Standard deviation and variance are in statistics but the formula for variance is somehow related to the L1 and L2.
Mathematically (L2 in machine learning sense),
$$Variance = \dfrac{(X_1-Mean)^2+..+ …
- 1,318
2
votes
1
answer
1k
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How to make DNN learn multiplication/division?
A single neuron with 2 weights and identity activation can learn addition/subtraction as the 2 weights will converge to 1 and 1 (addition), or 1 and -1 (subtraction).
However, for multiplication and …
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1
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1
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484
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Why is exp used in encoder of VAE instead of using the value of standard deviation alone?
There's one VAE example here:
https://towardsdatascience.com/teaching-a-variational-autoencoder-vae-to-draw-mnist-characters-978675c95776.
And the source code of encoder can be found at the followin …
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1
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1
answer
102
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What is the equation of the separation line for this neuron with identity activation?
I have a single neuron with 2 inputs, and identity activation, where f is activation function and u is output:
$u = f(w_1x_1 + w_2x_2 + b) = w_1x_1 + w_2x_2 + b$
My guessing for the separation line …
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12
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1
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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 for? …
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9
votes
1
answer
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What is the formula for the momentum and Adam optimisers?
In the gradient descent algorithm, the formula to update the weight $w$, which has $g$ as the partial gradient of the loss function with respect to it, is:
$$w\ -= r \times g$$
where $r$ is the learni …
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2
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0
answers
312
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Is Gradient Descent algorithm a part of Calculus of Variations?
As in https://en.wikipedia.org/wiki/Calculus_of_variations
The calculus of variations is a field of mathematical analysis that
uses variations, which are small changes in functions and functiona …
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1
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1
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226
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Random value generator using a single neuron or DNN
AI is supposed to do anything human or traditional computer can do, that is what we expect AI to be.
So 'generating random value' is also a task included in the scope that AI should be able to do
I …
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5
votes
3
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Why is symbolic AI not so popular as ANN but used by IBM's Deep Blue?
Everybody is implementing and using DNN with, for example, TensorFlow or PyTorch.
I thought IBM's Deep Blue was an ANN-based AI system, but this article says that IBM's Deep Blue was symbolic AI.
Are …
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1
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1
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What should the output of a neural network that needs to classify in an unsupervised fashion...
XOR data, without labels:
[[0,0],[0,1],[1,0],[1,1]]
I'm using this network for auto-classifying XOR data:
H1 <-- Dense(units=2, activation=relu) #any activation here
Z <-- Dense(units=2, activa …
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-1
votes
1
answer
341
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Is it necessary to standardise the expected output
Normalisation transform data into a range:
$$X_i = \dfrac{X_i - Min}{Max-Min}$$
Practically, I found out that the model doesn't generalise well when using normalisation of input data, instead of stand …
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2
votes
1
answer
2k
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How to avoid being stuck local optima in q-learning and q-network
When using the Bellman equation to update q-table or train q-network to fit greedy max values, the q-values very often get to the local optima and get stuck although randomization rate ($\epsilon$) ha …
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1
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What is the borderline between unsupervised learning and regular algorithms?
Unsupervised learning using neural networks is clearly machine learning since it is utilising neural nets.
However, some algorithms, k-means clustering, for example, are considered unsupervised learni …
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2
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1
answer
401
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GANs: Should Generator update weights when Discriminator says false continuously
My GANs is like this:
Train an autoencoder (VAE), get the decoder part and use as Generator
Train Discriminator
After training, do the generation in these steps:
Call Generator to generate an im …
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0
votes
1
answer
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Should the biases be zero or randomly initialised?
I'm initialising DNN of shape [2 inputs, 2 hiddens, 1 output] with these weights and biases:
#hidden layer
weight1= tf.Variable(tf.random_uniform([2,2], -1, 1),
name="layer1");
bias1 = tf.V …
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