Updated project 2

doc/Projects/2018/Project2/html/Project2.html

@@ -120,7 +120,7 @@ MathJax.Hub.Config({
 <center><b>Department of Physics, University of Oslo, Norway</b></center>
 <br>
 <p>
-<center><h4>Oct 9, 2018</h4></center> <!-- date -->
+<center><h4>Oct 12, 2018</h4></center> <!-- date -->
 <br>
 
 <h2 id="___sec0">Classification and Regression, from linear and logistic regression to neural networks  </h2>
@@ -385,7 +385,7 @@ standard gradient descent with a given learning rate, or even attempt
 to use the Newton-Raphson method.  Alternatively, it may be useful for
 the next part on neural networks to implement a stochastic gradient
 descent. For all  gradient methods, you can use <b>scikit-learn</b>'s toolbox for
-optimization methods instead of writing your own code.
+optimization methods in order to test your own results.
 
 <p>
 The notebook of <a href="https://physics.bu.edu/~pankajm/ML-Notebooks/HTML/NB_CVII-logreg_ising.html" target="_blank">Mehta et al</a> is highly recommended in order to benchmark your code and results.
@@ -403,10 +403,11 @@ to use the codes in the above lecture slides as starting points.
 
 <p>
 Train your network and compare the results with those from your linear regression code. 
-You can test your results against a similar code using _scikit_learn_ (see the examples in teh above lecture notes) or <b>tensorflow/keras</b>.
+You can test your results against a similar code using _scikit_learn_ (see the examples in the above lecture notes) or <b>tensorflow/keras</b>.
 
 <p>
 You should have the same elements as in the regression examples, including the \( R2 \) score, the MSE, and bootstrap or cross-validation.
+You may also need to think of using another activation function than the standard logistic function.
 
 <p>
 A useful reference on the back progagation algorithm is <a href="http://neuralnetworksanddeeplearning.com/" target="_blank">Nielsen's book</a>. It is an excellent read.