minor-bugs

morea/neural-networks/#experience-auto.md#

@@ -0,0 +1,40 @@
+---
+title: "Autoencoders"
+published: true
+morea_id: experience-auto
+morea_type: experience
+morea_summary: "Autoencoders and SVD"
+morea_start_date: "2021-07-15T23:00"
+morea_labels:
+---
+
+First review the section about eigenvalues and eigenspaces
+[here]. Recall that \({\mathbb R}^p\) represents the linear space of
+all vectors with \(p\) real coordinates. For a matrix \(n\times p\)
+matrix \(X\), one can use spectral decomposition of \(X^TX\)
+(respectively \(XX^T\)) to find an orthonormal basis for \({\mathbb
+R}^p\) (respectively \({\mathbb R}^n\)) using eigenvectors of \(X^TX\)
+(respectively \(XX^T\)), and therefore for the rows (respectively
+columns) of \(X\). Assume that \(n \ge p\), and let the eigenvalues
+of \(X^TX\) be \(\lambda_1\ge \lambda_2 \cdots \ge \lambda_p\), then
+the highest \(p\) eigenvalues of \(XX^T\) are also
+\(\lambda_1, \lambda_2 \upto \lambda_p\), while the remaining \(n-p\)
+eigenvalues are all 0. 
+
+
+Let \(V\) (respectively \(U\)) be the matrix formed
+by placing as columns the orthonormal basis obtained by the
+eigendecomposition of \(X^TX\) (respectively \(XX^T\)).
+Let \(\Sigma\)
+be the \(n\times p\) matrix formed with the positive square roots of
+the eigenvalues of \(X^TX\) in all the diagonal locations.
+The singular value decomposition observes that
+$$ X = U \Sigma V^T. $$
+
+The following
+[notebook](https://uhm-descartes.github.io/ee445/morea/neural-networks/autoencoder.ipynb)
+shows you how to build a neural network that recovers the singular
+value decomposition, the autoencoder. In the assessment, you will
+reuse the code, but with non-linear activations to project the MNIST
+dataset into a low dimensional manifold.
+

morea/neural-networks/.#experience-auto.md

@@ -0,0 +1 @@
+[email protected]:1683013381

morea/neural-networks/assessment-nn.md

@@ -1,14 +1,28 @@
 ---
-title: "CHANGE ME"
-published: false
-morea_id: assessment-CHANGE-ME
-morea_summary: "CHANGE ME"
+title: "Assessment"
+published: true
+morea_id: assessment-nn
+morea_summary: "Neural Networks"
 morea_outcomes_assessed:
  # - outcome-CHANGE-ME
 morea_type: assessment
 morea_start_date: "2021-07-16T09:00"
 morea_labels:
 ---
-# CHANGE ME
+# Problems
 
-TBD
+* Use a feedforward architecture to train and predict on the CIFAR-10
+  and Fashion-MNIST dataset. Here, you may need to use dropout to
+  train better and reduce overfitting---find out how to implement this
+  technique. We discussed dropout very briefly in class, so you may want
+  to look up Dropout techniques online for more background.
+
+* Project the MNIST dataset into as small a manifold as
+  possible. Meaning, you should come up with two transformations (we
+  will call them encoder and decoder). The encoder should represent
+  each \(28\times 28\) test image into a small vector (you can have
+  this vector have less than 10 coordinates, but it is ok if your
+  output is slightly larger too), but that doesn't lose
+  information---namely the decoder can reconstruct the original image
+  (with negligible loss) from the small vector output from the
+  encoder.

morea/neural-networks/assessment-nn.md~

@@ -0,0 +1,14 @@
+---
+title: "CHANGE ME"
+published: false
+morea_id: assessment-CHANGE-ME
+morea_summary: "CHANGE ME"
+morea_outcomes_assessed:
+ # - outcome-CHANGE-ME
+morea_type: assessment
+morea_start_date: "2021-07-16T09:00"
+morea_labels:
+---
+# CHANGE ME
+
+TBD

morea/neural-networks/module-nn.md

@@ -18,7 +18,7 @@ morea_icon_url: /morea/CHANGE-ME/CHANGE-ME.png
 morea_start_date: "2021-07-12"
 morea_end_date: "2021-07-16"
 morea_labels:
-morea_sort_order: 21
+morea_sort_order: 81
 ---
 
 CHANGE ME