What are the best books to study Neural Networks from a purely mathematical perspective?












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I am looking for a book that goes through the mathematical aspects of neural networks, from simple forward passage of multilayer perceptron in matrix form or differentiation of activation functions, to back propagation in CNN or RNN (to mention some of the topics).



Do you know any book that goes in depth into this theory? I've had a look at a couple (such as Pattern Recognition and Machine Learning by Bishop) but still have not found a rigorous one (with exercises would be a plus). Do you have any suggestions?






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    $begingroup$


    I am looking for a book that goes through the mathematical aspects of neural networks, from simple forward passage of multilayer perceptron in matrix form or differentiation of activation functions, to back propagation in CNN or RNN (to mention some of the topics).



    Do you know any book that goes in depth into this theory? I've had a look at a couple (such as Pattern Recognition and Machine Learning by Bishop) but still have not found a rigorous one (with exercises would be a plus). Do you have any suggestions?






    matrices book-recommendation mathematical-modeling neural-networks







    share|cite|improve this question







    New contributor




    Eli is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







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      5












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      5


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      $begingroup$


      I am looking for a book that goes through the mathematical aspects of neural networks, from simple forward passage of multilayer perceptron in matrix form or differentiation of activation functions, to back propagation in CNN or RNN (to mention some of the topics).



      Do you know any book that goes in depth into this theory? I've had a look at a couple (such as Pattern Recognition and Machine Learning by Bishop) but still have not found a rigorous one (with exercises would be a plus). Do you have any suggestions?






      matrices book-recommendation mathematical-modeling neural-networks







      share|cite|improve this question







      New contributor




      Eli is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      I am looking for a book that goes through the mathematical aspects of neural networks, from simple forward passage of multilayer perceptron in matrix form or differentiation of activation functions, to back propagation in CNN or RNN (to mention some of the topics).



      Do you know any book that goes in depth into this theory? I've had a look at a couple (such as Pattern Recognition and Machine Learning by Bishop) but still have not found a rigorous one (with exercises would be a plus). Do you have any suggestions?






      matrices book-recommendation mathematical-modeling neural-networks




      matrices book-recommendation mathematical-modeling neural-networks






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          2 Answers
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          For MLPs, there is a rigorous derivation in the optimization textbook by Edwin Chong and Zak. Although it is notation heavy as all things related to neural networks must be.



          This book is for some reason freely available online. See page 219 of https://eng.uok.ac.ir/mfathi/Courses/Advanced%20Eng%20Math/An%20Introduction%20to%20Optimization-%20E.%20Chong,%20S.%20Zak.pdf



          I think there is essentially no good mathematical textbook on convolutional neural networks or RNN in existence. People essentially just base their intuition off of MLPs. But it is not hard to create a mathematically rigorous derivation of forward and backward propagation of CNN or RNN.






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            I'd recommend Deep Learning by Goodfellow, Bengio and Courville. I don't know if I'd call it "purely mathematical", but it covers a good amount of math background in the first few chapters. No exercises, though.






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              Thank you - I've actually had a look at that one too, but while it is good in introducing the main mathematical tools needed for NN, I found it a bit lacking when it came to properly develop the model mathematically.
              $endgroup$
              – Eli
              2 hours ago











            Your Answer





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            2 Answers
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            2 Answers
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            3












            $begingroup$

            For MLPs, there is a rigorous derivation in the optimization textbook by Edwin Chong and Zak. Although it is notation heavy as all things related to neural networks must be.



            This book is for some reason freely available online. See page 219 of https://eng.uok.ac.ir/mfathi/Courses/Advanced%20Eng%20Math/An%20Introduction%20to%20Optimization-%20E.%20Chong,%20S.%20Zak.pdf



            I think there is essentially no good mathematical textbook on convolutional neural networks or RNN in existence. People essentially just base their intuition off of MLPs. But it is not hard to create a mathematically rigorous derivation of forward and backward propagation of CNN or RNN.






            share|cite|improve this answer











            $endgroup$


















              3












              $begingroup$

              For MLPs, there is a rigorous derivation in the optimization textbook by Edwin Chong and Zak. Although it is notation heavy as all things related to neural networks must be.



              This book is for some reason freely available online. See page 219 of https://eng.uok.ac.ir/mfathi/Courses/Advanced%20Eng%20Math/An%20Introduction%20to%20Optimization-%20E.%20Chong,%20S.%20Zak.pdf



              I think there is essentially no good mathematical textbook on convolutional neural networks or RNN in existence. People essentially just base their intuition off of MLPs. But it is not hard to create a mathematically rigorous derivation of forward and backward propagation of CNN or RNN.






              share|cite|improve this answer











              $endgroup$
















                3












                3








                3





                $begingroup$

                For MLPs, there is a rigorous derivation in the optimization textbook by Edwin Chong and Zak. Although it is notation heavy as all things related to neural networks must be.



                This book is for some reason freely available online. See page 219 of https://eng.uok.ac.ir/mfathi/Courses/Advanced%20Eng%20Math/An%20Introduction%20to%20Optimization-%20E.%20Chong,%20S.%20Zak.pdf



                I think there is essentially no good mathematical textbook on convolutional neural networks or RNN in existence. People essentially just base their intuition off of MLPs. But it is not hard to create a mathematically rigorous derivation of forward and backward propagation of CNN or RNN.






                share|cite|improve this answer











                $endgroup$



                For MLPs, there is a rigorous derivation in the optimization textbook by Edwin Chong and Zak. Although it is notation heavy as all things related to neural networks must be.



                This book is for some reason freely available online. See page 219 of https://eng.uok.ac.ir/mfathi/Courses/Advanced%20Eng%20Math/An%20Introduction%20to%20Optimization-%20E.%20Chong,%20S.%20Zak.pdf



                I think there is essentially no good mathematical textbook on convolutional neural networks or RNN in existence. People essentially just base their intuition off of MLPs. But it is not hard to create a mathematically rigorous derivation of forward and backward propagation of CNN or RNN.







                share|cite|improve this answer














                share|cite|improve this answer



                share|cite|improve this answer








                edited 1 hour ago

























                answered 1 hour ago









                Shamisen ExpertShamisen Expert

                2,81821945




                2,81821945























                    2












                    $begingroup$

                    I'd recommend Deep Learning by Goodfellow, Bengio and Courville. I don't know if I'd call it "purely mathematical", but it covers a good amount of math background in the first few chapters. No exercises, though.






                    share|cite|improve this answer









                    $endgroup$









                    • 1




                      $begingroup$
                      Thank you - I've actually had a look at that one too, but while it is good in introducing the main mathematical tools needed for NN, I found it a bit lacking when it came to properly develop the model mathematically.
                      $endgroup$
                      – Eli
                      2 hours ago
















                    2












                    $begingroup$

                    I'd recommend Deep Learning by Goodfellow, Bengio and Courville. I don't know if I'd call it "purely mathematical", but it covers a good amount of math background in the first few chapters. No exercises, though.






                    share|cite|improve this answer









                    $endgroup$









                    • 1




                      $begingroup$
                      Thank you - I've actually had a look at that one too, but while it is good in introducing the main mathematical tools needed for NN, I found it a bit lacking when it came to properly develop the model mathematically.
                      $endgroup$
                      – Eli
                      2 hours ago














                    2












                    2








                    2





                    $begingroup$

                    I'd recommend Deep Learning by Goodfellow, Bengio and Courville. I don't know if I'd call it "purely mathematical", but it covers a good amount of math background in the first few chapters. No exercises, though.






                    share|cite|improve this answer









                    $endgroup$



                    I'd recommend Deep Learning by Goodfellow, Bengio and Courville. I don't know if I'd call it "purely mathematical", but it covers a good amount of math background in the first few chapters. No exercises, though.







                    share|cite|improve this answer












                    share|cite|improve this answer



                    share|cite|improve this answer










                    answered 2 hours ago









                    Jair TaylorJair Taylor

                    9,05432144




                    9,05432144








                    • 1




                      $begingroup$
                      Thank you - I've actually had a look at that one too, but while it is good in introducing the main mathematical tools needed for NN, I found it a bit lacking when it came to properly develop the model mathematically.
                      $endgroup$
                      – Eli
                      2 hours ago














                    • 1




                      $begingroup$
                      Thank you - I've actually had a look at that one too, but while it is good in introducing the main mathematical tools needed for NN, I found it a bit lacking when it came to properly develop the model mathematically.
                      $endgroup$
                      – Eli
                      2 hours ago








                    1




                    1




                    $begingroup$
                    Thank you - I've actually had a look at that one too, but while it is good in introducing the main mathematical tools needed for NN, I found it a bit lacking when it came to properly develop the model mathematically.
                    $endgroup$
                    – Eli
                    2 hours ago




                    $begingroup$
                    Thank you - I've actually had a look at that one too, but while it is good in introducing the main mathematical tools needed for NN, I found it a bit lacking when it came to properly develop the model mathematically.
                    $endgroup$
                    – Eli
                    2 hours ago










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