-
@@ -429,7 +429,7 @@ 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 scikit-learn's toolbox for -optimization methods instead of writing your own code. +optimization methods in order to test your own results.
The notebook of Mehta et al is highly recommended in order to benchmark your code and results. @@ -447,10 +447,11 @@ to use the codes in the above lecture slides as starting points.
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 tensorflow/keras. +You can test your results against a similar code using _scikit_learn_ (see the examples in the above lecture notes) or tensorflow/keras.
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.
A useful reference on the back progagation algorithm is Nielsen's book. It is an excellent read. diff --git a/doc/Projects/2018/Project2/html/Project2.html b/doc/Projects/2018/Project2/html/Project2.html index ecd23a7b8..7e8da5aaa 100644 --- a/doc/Projects/2018/Project2/html/Project2.html +++ b/doc/Projects/2018/Project2/html/Project2.html @@ -120,7 +120,7 @@ MathJax.Hub.Config({
-
The notebook of Mehta et al 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.
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 tensorflow/keras. +You can test your results against a similar code using _scikit_learn_ (see the examples in the above lecture notes) or tensorflow/keras.
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.
A useful reference on the back progagation algorithm is Nielsen's book. It is an excellent read. diff --git a/doc/Projects/2018/Project2/ipynb/ipynb-Project2-src.tar.gz b/doc/Projects/2018/Project2/ipynb/ipynb-Project2-src.tar.gz index f0df78888..23ce22ad2 100644 Binary files a/doc/Projects/2018/Project2/ipynb/ipynb-Project2-src.tar.gz and b/doc/Projects/2018/Project2/ipynb/ipynb-Project2-src.tar.gz differ diff --git a/doc/Projects/2018/Project2/pdf/Project2.p.tex b/doc/Projects/2018/Project2/pdf/Project2.p.tex index 435f1b20d..511dfff7e 100644 --- a/doc/Projects/2018/Project2/pdf/Project2.p.tex +++ b/doc/Projects/2018/Project2/pdf/Project2.p.tex @@ -155,7 +155,7 @@ Project 2 on Machine Learning, deadline November 5 % --- begin date --- \begin{center} -Oct 9, 2018 +Oct 12, 2018 \end{center} % --- end date --- @@ -401,7 +401,7 @@ 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 \textbf{scikit-learn}'s toolbox for -optimization methods instead of writing your own code. +optimization methods in order to test your own results. The notebook of \href{{https://physics.bu.edu/~pankajm/ML-Notebooks/HTML/NB_CVII-logreg_ising.html}}{Mehta et al} is highly recommended in order to benchmark your code and results. @@ -416,10 +416,10 @@ now the network to find the optimal weights and biases. You are free to use the codes in the above lecture slides as starting points. 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 \textbf{tensorflow/keras}. +You can test your results against a similar code using _scikit_learn_ (see the examples in the above lecture notes) or \textbf{tensorflow/keras}. 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. A useful reference on the back progagation algorithm is \href{{http://neuralnetworksanddeeplearning.com/}}{Nielsen's book}. It is an excellent read. diff --git a/doc/Projects/2018/Project2/pdf/Project2.pdf b/doc/Projects/2018/Project2/pdf/Project2.pdf index 668bc511c..7e7fbf6cb 100644 Binary files a/doc/Projects/2018/Project2/pdf/Project2.pdf and b/doc/Projects/2018/Project2/pdf/Project2.pdf differ diff --git a/doc/Projects/2018/Project2/pdf/Project2.tex b/doc/Projects/2018/Project2/pdf/Project2.tex index 1f165fe84..1cf9e34d3 100644 --- a/doc/Projects/2018/Project2/pdf/Project2.tex +++ b/doc/Projects/2018/Project2/pdf/Project2.tex @@ -125,7 +125,7 @@ Project 2 on Machine Learning, deadline November 5 % --- begin date --- \begin{center} -Oct 9, 2018 +Oct 12, 2018 \end{center} % --- end date --- @@ -371,7 +371,7 @@ 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 \textbf{scikit-learn}'s toolbox for -optimization methods instead of writing your own code. +optimization methods in order to test your own results. The notebook of \href{{https://physics.bu.edu/~pankajm/ML-Notebooks/HTML/NB_CVII-logreg_ising.html}}{Mehta et al} is highly recommended in order to benchmark your code and results. @@ -386,10 +386,10 @@ now the network to find the optimal weights and biases. You are free to use the codes in the above lecture slides as starting points. 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 \textbf{tensorflow/keras}. +You can test your results against a similar code using _scikit_learn_ (see the examples in the above lecture notes) or \textbf{tensorflow/keras}. 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. A useful reference on the back progagation algorithm is \href{{http://neuralnetworksanddeeplearning.com/}}{Nielsen's book}. It is an excellent read. diff --git a/doc/src/Projects/2018/Project2/Project2.do.txt b/doc/src/Projects/2018/Project2/Project2.do.txt index 39ce09f20..621311326 100644 --- a/doc/src/Projects/2018/Project2/Project2.do.txt +++ b/doc/src/Projects/2018/Project2/Project2.do.txt @@ -255,7 +255,7 @@ 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 _scikit-learn_'s toolbox for -optimization methods instead of writing your own code. +optimization methods in order to test your own results. The notebook of "Mehta et al":"https://physics.bu.edu/~pankajm/ML-Notebooks/HTML/NB_CVII-logreg_ising.html" is highly recommended in order to benchmark your code and results. @@ -271,10 +271,10 @@ now the network to find the optimal weights and biases. You are free to use the codes in the above lecture slides as starting points. 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 _tensorflow/keras_. +You can test your results against a similar code using _scikit_learn_ (see the examples in the above lecture notes) or _tensorflow/keras_. 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. A useful reference on the back progagation algorithm is "Nielsen's book":"http://neuralnetworksanddeeplearning.com/". It is an excellent read.