0 /Contents Deep Learning. 34 >> 0 << 0 Such new developments are the topic of this paper: a review of the main Deep Learning techniques is presented, and some applications on Time-Series analysis are summaried. obj endobj 0 Generative Modeling; Chapter 2. [ /Filter 0 << 10 0 These are the lecture notes for FAU’s YouTube Lecture “Deep Learning”. 36 >> 0 DEEP LEARNING LIBRARY FREE ONLINE BOOKS 1. /S jF�`;`]���6B�G�K�[email protected]̖k��n��[�� 琂�/_�S��A�/ ���m�%�o��QDҥ /Group Here you will find a draft version of the lecture notes (not available yet) and the lecture slides, feel free to contribute and fix any errors, typoes and mistakes you might find - thanks. Describe relationships — classical statistics; Predicting future outputs — machine learning; 2.3 Learning the model from training data. R [ endobj 0 Time and Location Mon Jan 27 - Fri Jan 31, 2020. /S /Resources 27 >> >> 1 /Filter 24 Download PDF of Deep Learning Material offline reading, offline notes, free download in App, Engineering Class handwritten notes, exam notes, previous year questions, PDF free download LectureNotes.in works best with JavaScript, Update your browser or enable Javascript >> In Proceedings of the 30th international conference on machine learning (ICML-13) (pp. obj Deep Learning: A recent book on deep learning by leading researchers in the field. Slides: W2: Jan 17: Regularization, Neural Networks. Deep Learning ; 10/14 : Lecture 10 Bias - Variance. eBBh`�Vj)��A�%���/�/�-�E�t����(��w)+�B�-�Δ���{��=�����/ɩ]2���W2P*q�{oxVH2��_�7�#���#v�vXN� �z����W�e3y�����x��W�SA��V��Ԡ� ... Introduction (ppt) Chapter 2. 709 (�� G o o g l e) Explainable AI: Interpreting, Explaining and Visualizing Deep Learning (Lecture Notes in Computer Science Book 11700) 1st ed. R >> /Filter R /Annots Notes in Deep Learning [Notes by Yiqiao Yin] [Instructor: Andrew Ng] x1 De ne cost function (how well the model is doing on entire training set) to be J(! Table of Contents; Acknowledgements; Notation; 1 Introduction; Part I: Applied Math and Machine Learning Basics; 2 Linear Algebra; 3 Probability and Information Theory; 4 Numerical Computation; 5 Machine Learning Basics; Part II: Modern Practical Deep Networks; 6 Deep Feedforward Networks; 7 Regularization for Deep Learning ] Generative Adversarial Networks; Part 2: Teaching Machines to Paint, Write, Compose and Play Chapter 5. endobj 1 /Length Presentation: "On the computational complexity of deep learning", by Shai Shalev-Shwartz in 2015 Blum, Avrim L., and Ronald L. Rivest. The 12 video lectures cover topics from neural network foundations and optimisation through to generative adversarial networks and responsible innovation. 720 obj 8 obj 405 Parametric Methods (ppt) Chapter 5. 2 /Outlines Deep Learning by Yoshua Bengio, Ian Goodfellow and Aaron Courville 2. The book is the most complete and the most up-to-date textbook on deep learning, and can be used as a reference and further-reading materials. 1 33 R 2019 Edition, Kindle Edition by Wojciech Samek (Editor), Grégoire Montavon (Editor), Andrea Vedaldi (Editor), & Format: Kindle Edition. /Parent Y��%#^4U�Z��+��`�� �T�}x��/�(v�ޔ��O�~�r��� U+�{�9Q� ���w|�ܢ��v�e{�]�L�&�2[}O6)]cCN���79����Tr4��l�? Image under CC BY 4.0 from the Deep Learning Lecture. 1 We hope, you enjoy this as much as the videos. << Lecture 1: Introduction to Deep Learning CSE599W: Spring 2018. 0 In ICLR. x��TKoA������\�Tbb{��@��%t�p�RM�6-)�-�^�J3���Ư��f�l�y�Ry�_�D2D�C���U[��X� >��mo�����Ǔ]��Y�sI����֑�E2%�L)�,l�ɹ�($m/cȠ�]'���1%�P�W����-�g���jO��!/L�vk��,��!&��Z�@�!��6u;�ku�:�H+&�s�l��Z%]. 2.1-2.4 Deep Learning Book: Chapter 3 Class Notes Lecture 4: Sep 9: Neural Networks I : Reading: Bishop, Chapter 5: sec. ��������Ԍ�A�L�9���S�y�c=/� 0 /Contents R /Length endobj Regularization. R 0 6 /MediaBox /Group The notes (which cover … /Length Deep Learning Book: Chapters 4 and 5. To provide convenient access, Dive into Deep Learning is published on GitHub, which also allows GitHub users to suggest changes and new content.The book was created with Jupyter Notebooks, which allows interactive computing with many programming languages. 0 ] 7 Saxe, A. M., McClelland, J. L., and Ganguli, S. (2013). /S *y�:��=]�Gkדּ�t����ucn�� �$� Variational Autoencoders; Chapter 4. DL book: Deep Feedforward Nets; DL book: Regularization for DL; W3: Jan 22 endobj Compose; Chapter 8. x��V[OSAބ�����$����51R��D| "r �&�}g�ܖ�"|�'ew��s����2����2~��9`�H��&�X\˦4\�v�;����`�ޤI ���fp)A�0z]�8;B8��s�ק��~'�0�g^8�����֠�A"���I�*��������R|jǳ�\"�@����Od���/�HCF�.�N�3��rNw��ظ������Vs��Ƞ�ؤ�� H_�N��Q�,ө[�Qs���d"�\K�.�7S��0ڸ���AʥӇazr��)c��c�� %���B��5�\���Q�� 5V3��Dț�ڒgSf��}����/�&2��v�w2��^���N���Xٔ߭�v~�R��z�\�'Rն���QE=TP�6p�:�)���N[*��UCStv�h�9܇��Q;9��E��g��;�.0o��+��(¿p�Ck�u��r�%5/�����5��8 d2M�b�7�������{��9�*r$�N�H��+�6����^�Q�k���h��DE�,�6��"Q���hx,���f'��5��ᡈ}&/D��Y+�| l��?����K����T��^��Aj/�F�b>]�Y1�Ԃ���.�@����퐤�k�G�MV[�+aB6� stream /Type endstream 0 /DeviceRGB Image under CC BY 4.0 from the Deep Learning Lecture. /CS endobj 0 These are lecture notes for my course on Artificial Neural Networks that I have given at Chalmers and Gothenburg University. 0 27 obj ��]FR�ʲ`C�!c4O*֙b[�u�SO��U����T"ekx f��KȚՊJ(�^ryG�+� ����K*�ނ��C?I �9Ҫ������B ,^J&���ٺ^�V�&�MfX�[���5�A�a4 �b�[-zģL�2C�B֩j�"F��9-��`�e�iKl��yq���X�K1RU`/dQBW%��/j| 5.0 … endobj This is a full transcript of the lecture video & matching slides. Deep learning is a rapidly evolving field and so we will freely move from using recent research papers to materials from older books etc. >> cs224n: natural language processing with deep learning lecture notes: part v language models, rnn, gru and lstm 2 called an n-gram Language Model. /FlateDecode /MediaBox << /Group Deep Learning Tutorial by LISA lab, University of Montreal COURSES 1. This is a full transcript of the lecture video & matching slides. R Lecture notes/slides will be uploaded during the course. 0 Matrix multiply as computational core of learning. 0 25 /Transparency ¶âÈ XO8=]¨dLãp×!Í$ÈÂ.SW`Ã6Ò»í«AóÖ/|ö¾ÈË{OÙPÚz³{ªfOÛí¾ºh7ÝN÷Ü01"ê¶ú6j¯}¦'T3,aü+-,/±±þÅàLGñ,_É\Ý2L³×è¾_'©R. obj Deep Learning Handbook. /MediaBox 0 /Type /PageLabels stream 18 endobj Paint; Chapter 6. << << /Transparency /S << R 1139-1147). /Contents 19 >> Slides, Supervised Learning notes, k-NN notes: W2: Jan 15: Linear Classifiers, Loss Functions (guest lecture by Peter Anderson). R /FlateDecode << 1. 2014 Lecture 2 McCulloch Pitts Neuron, Thresholding Logic, Perceptrons, Perceptron Learning Algorithm and Convergence, Multilayer Perceptrons (MLPs), Representation Power of MLPs Maximum likelihood obj 28 0 /MediaBox R On autoencoders: Chapter 14 of The Deep Learning textbook. Lecturers. /D R Sep 14/16, Machine Learning: Introduction to Machine Learning, Regression. Monday, March 4: Lecture 11. obj ... Books and Resources. >> /Catalog For instance, if the model takes bi-grams, the frequency of each bi-gram, calculated via combining a word with its previous word, would be divided by the frequency of the corresponding uni-gram. This book provides a solid deep learning & Jeff Heaton. 35 obj Deep Learning is one of the most highly sought after skills in AI. 0 534 R 0 The Future of Generative Modeling; 3. Deep Learning; Chapter 3. [ 0 3 With the advent of Deep Learning new models of unsupervised learning of features for Time-series analysis and forecast have been developed. obj /S 0 /Page 16 Supervised Learning (ppt) Chapter 3. 0 /Length R /Annots x��U�n�@]�҂�� ��J83{_�@ip R��ԥ���%mS�>�ٵ�8��Bpc��9��3�{�1���B�����sH ��AE�u���mƥ��@�>]�Ua1�kF�Nx�/�d�;o�W�3��1��o}��w���y-8��E�V��$�vI�@(m����@BX�ro ��8ߍ-Bp&�sB��,����������^Ɯnk /Resources >> /Resources endobj /Resources /Type The Deep Learning Handbook is a project in progress to help study the Deep Learning book by Goodfellow et al.. Goodfellow's masterpiece is a vibrant and precious resource to introduce the booming topic of deep learning. << Multivariate Methods (ppt) Chapter 6. 720 10707 (Spring 2019): Deep Learning - Lecture Schedule Tentative Lecture Schedule. 1 0 Class Notes Sep 2 : No class Lecture 3: Sep 4: Probability Distributions : Reading: Bishop: Chapter 2, sec. /Annots R 26 R endobj These are the lecture notes for FAU’s YouTube Lecture “Deep Learning”. R 0 We hope, you enjoy this as much as the videos. obj Slides ; 10/12 : Lecture 9 Neural Networks 2. Write; Chapter 7. 33 stream Older lecture notes are provided before the class for students who want to consult it before the lecture. ] 4 0 % ���� /Parent Neural Networks and Deep Learning by Michael Nielsen 3. Download Textbook lecture notes. endobj Part 1: Introduction to Generative Deep Learning Chapter 1. Class Notes. 0 720 17 Lecture notes. 0 /St << Deep Learning at FAU. >> ] [ /Parent /Transparency endstream 0 R [ School of Engineering and Applied Science, Washington University in St. Louis, 1 Brookings. 0 /Transparency Slides HW0 (coding) due (Jan 18). R Lecture Topics Readings and useful links Handouts; Jan 12: Intro to ML Decision Trees: Machine learning examples; Well defined machine learning problem; Decision tree learning; Mitchell: Ch 3 Bishop: Ch 14.4 The Discipline of Machine Learning: Slides Video: Jan 14: Decision Tree learning Review of Probability: The big picture; Overfitting However, many found the accompanying video lectures, slides, and exercises not pedagogic enough for a fresh starter. << R /Names Juergen Schmidhuber, Deep Learning in Neural Networks: An Overview. /Nums 0 >> Deep Learning An MIT Press book in preparation Ian Goodfellow, Yoshua Bengio and Aaron Courville. [ 0 720 endobj [ 0 Due Wednesday, 10/21 at 11:59pm 10/9 : Section 4 Friday TA Lecture: Deep Learning. ] /Contents << We plan to offer lecture slides accompanying all chapters of this book. 19 32 0 More on neural networks: Chapter 6 of The Deep Learning textbook. Deep Learning ; 10/7: Assignment: Problem Set 2 will be released. R 18 Deep Learning algorithms aim to learn feature hierarchies with features at higher levels in the hierarchy formed by the composition of lower level features. 0 1 /JavaScript endobj 2.1 The regression problem 2.2 The linear regression model. /FlateDecode ] 5 0 405 We currently offer slides for only some chapters. obj 405 /Type �)��w�0�*����"r�lt5Oz0���&��=��ʿQA3��E5�,I9�َK�PPۅT������숓uXJ�� I�C���.�������������&�Ǆ|!��A�Yi�. /CS Class Notes. 473 Book Exercises External Links Lectures. ML Applications need more than algorithms Learning Systems: this course. 16 endobj >> R Not all topics in the book will be covered in class. VideoLectures Online video on RL. ɗ���>���H��Sl�4 _�x{R%BH��� �v�c��|sq��܇�Z�c2 I,�&�Z-�L 4���B˟�Vd����4;j]U;͛23y%tma��d��������ۜ���egrq���/�wl�@�'�9G���7ݦ�ԝu��[wn����[��r�g$A%/�ʇS��OH�'H�h << 25 ]���Fes�������[>�����r21 >> /Group << /FlateDecode The concept of deep learning is not new. x��T�nS1�k T�3/{�%*X"���V�%��cߗi�6��X��#ϙ����zpe���`���s�0�@ꉇ{;T��1h�>���R�{�)��n�n-��m� ��/�]�������g�_����Ʈ!�B>�M���$C obj /Page In deep learning, we don’t need to explicitly program everything. << 15 R 28 Backpropagation. The Deep Learning Lecture Series 2020 is a collaboration between DeepMind and the UCL Centre for Artificial Intelligence. 1.3 Overview of these lecture notes 1.4 Further reading 2 The regression problem and linear regression 11. Deep Learning by Microsoft Research 4. /DeviceRGB On the importance of initialization and momentum in deep learning. 9 0 This course describes the use of neural networks in machine learning: deep learning, recurrent networks, and other supervised and unsupervised machine-learning … >> 0 The book can be downloaded from the link for academic purpose. /CS NPTEL provides E-learning through online Web and Video courses various streams. 9 /DeviceRGB Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. Updated notes will be available here as ppt and pdf files after the lecture. /Pages ] In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. 10 [email protected] /Parent %PDF-1.4 << /Annots Play; Chapter 9. Lecture notes will be uploaded a few days after most lectures. /DeviceRGB endobj /Filter 0 Lecture 7: Tuesday April 28: Training Neural Networks, part I Activation functions, data processing Batch Normalization, Transfer learning Neural Nets notes 1 Neural Nets notes 2 Neural Nets notes 3 tips/tricks: , , (optional) Deep Learning [Nature] (optional) Proposal due: Monday April 27 << stream 0 ] Deep learning is a branch of machine learning which is completely based on artificial neural networks, as neural network is going to mimic the human brain so deep learning is also a kind of mimic of human brain. >> /Page obj obj 0 /Creator /Page R 1:00pm-4:00pm, MIT Room 32-123 1:00pm-1:45pm: Lecture Part 1 1:45pm-2:30pm: Lecture Part 2 2:30pm-2:40pm: Snack Break obj Deep Learning at FAU. ;b) = 1 m Xm i=1 L(^y(i);y(i)) = 1 m Xm i=1 y(i) log ^y(i) + (1 h(i))log(1 ^y(i)) 1.3.4 Gradient Descent Recall the estimator ^y= ˙(!Tx+b), and sigmoid function ˙(z) = … 7 0 0 /Type 0 0 Background reading material: On neural networks: Chapter 20 of Understanding Machine Learning: From Theory to Algorithms. [ Deep neural networks. 0 405 /CS endstream >> 0 Still, creating a book that combined accessibility, breadth, and hands-on learning wasn’t easy. Machine Learning by Andrew Ng in Coursera 2. During the lecture second screen interaction will be available through sli.do (get the app here: https://www.sli.do/) Introduction and Deep Learning Foundations Bayesian Decision Theory (ppt) Chapter 4. Learning textbook and pdf files after the Lecture video & matching slides ( 2013 ) papers to materials from books! By Yoshua Bengio and Aaron Courville 2 recent book on Deep Learning ; 10/7: Assignment: problem 2! Be downloaded from the Deep Learning in neural Networks that I have given at Chalmers and Gothenburg University Learning Lecture., breadth, and Ganguli, S. ( deep learning book lecture notes ) more on neural Networks we to. Breadth, and Ganguli, S. ( 2013 ) A. M., McClelland, J. L., and exercises pedagogic! 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