done stuff

This commit is contained in:
Claudio Maggioni 2021-05-05 18:07:38 +02:00
parent 68712ae0e0
commit 30680d799f
2 changed files with 14 additions and 4 deletions

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@ -200,10 +200,21 @@ Comment and compare how the (a.) training error, (b.) test error and
The classification problem in the graph, according to the data points
shown, is quite similar to the XOR or ex-or problem. Since in 1969 that
problem was proved impossible to solve by a perceptron model by Minsky and
Papert, then the
Papert, then that would be quite a motivation in front of my boss.
1. **Would you expect to have better luck with a neural network with
On a morev general (and more serious) note, the perceptron model would be
unable to solve the problem in the picture since a perceptron can solve only
linearly-separable classification problems, and even through a simple
graphical argument we would be unable to find a line able able to separate
yellow and purple dots w.r.t. a decent approximation simply due to the way
the dots are positioned.
2. **Would you expect to have better luck with a neural network with
activation function $h(x) = - x \cdot e^{-2}$ for the hidden units?**
1. **What are the main differences and similarities between the
Boh
3. **What are the main differences and similarities between the
perceptron and the logistic regression neuron?**

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@ -23,7 +23,6 @@ points = 2000
lr = LinearRegression(fit_intercept=False)
# Build x feature vector with columns for theta_3 and theta_4
# variable name explained here: https://vimeo.com/380021022
X = np.zeros([points, 5])
X[:, 0] = 1
X[:, 1:3] = xs