/Annots R 0 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. /Pages /Annots 0 obj 10707 (Spring 2019): Deep Learning - Lecture Schedule Tentative Lecture Schedule. /DeviceRGB In deep learning, we don’t need to explicitly program everything. /Resources /CS stream 0 /Length 0 0 obj 0 0 obj R 720 These are the lecture notes for FAU’s YouTube Lecture “Deep Learning”. (�� G o o g l e) endobj Juergen Schmidhuber, Deep Learning in Neural Networks: An Overview. This book provides a solid deep learning & Jeff Heaton. ] R endobj 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� Deep Learning An MIT Press book in preparation Ian Goodfellow, Yoshua Bengio and Aaron Courville. 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. 0 Slides, Supervised Learning notes, k-NN notes: W2: Jan 15: Linear Classifiers, Loss Functions (guest lecture by Peter Anderson). 0 /Resources Book Exercises External Links Lectures. R 9 /Type 534 0 obj Class Notes Sep 2 : No class Lecture 3: Sep 4: Probability Distributions : Reading: Bishop: Chapter 2, sec. obj 0 720 0 /St R ... Books and Resources. endobj 0 0 Explainable AI: Interpreting, Explaining and Visualizing Deep Learning (Lecture Notes in Computer Science Book 11700) 1st ed. [ Lecturers. 720 Slides: W2: Jan 17: Regularization, Neural Networks. /Page /MediaBox /D /S 473 [ 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%]. 0 35 Deep Learning. /FlateDecode 28 In Proceedings of the 30th international conference on machine learning (ICML-13) (pp. obj We currently offer slides for only some chapters. 18 32 % ���� Background reading material: On neural networks: Chapter 20 of Understanding Machine Learning: From Theory to Algorithms. 28 The 12 video lectures cover topics from neural network foundations and optimisation through to generative adversarial networks and responsible innovation. 33 /Length endobj obj Sep 14/16, Machine Learning: Introduction to Machine Learning, Regression. Paint; Chapter 6. DEEP LEARNING LIBRARY FREE ONLINE BOOKS 1. Parametric Methods (ppt) Chapter 5. Not all topics in the book will be covered in class. 4 25 26 ... Introduction (ppt) Chapter 2. /Group 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. >> /Catalog Play; Chapter 9. Class Notes. Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. /Parent 36 0 10 0 0 In ICLR. /Nums /Transparency 1139-1147). << /FlateDecode We plan to offer lecture slides accompanying all chapters of this book. 405 0 endobj 0 /Length 1.3 Overview of these lecture notes 1.4 Further reading 2 The regression problem and linear regression 11. /Page obj endstream 0 ��������Ԍ�A�L�9���S�y�c=/� These are lecture notes for my course on Artificial Neural Networks that I have given at Chalmers and Gothenburg University. 0 5 << endobj Lecture notes will be uploaded a few days after most lectures. /Group [ 0 0 Variational Autoencoders; Chapter 4. %PDF-1.4 16 endobj 24 0 R >> R /Contents 0 8 >> >> 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 /MediaBox /Page With the advent of Deep Learning new models of unsupervised learning of features for Time-series analysis and forecast have been developed. 0 0 << /Transparency 2.1-2.4 Deep Learning Book: Chapter 3 Class Notes Lecture 4: Sep 9: Neural Networks I : Reading: Bishop, Chapter 5: sec. 1 /Group << 0 /Length >> endobj /Creator /Filter /Contents endstream Deep Learning; Chapter 3. The book can be downloaded from the link for academic purpose. 0 << 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 /CS Deep Learning at FAU. 0 Deep learning is a rapidly evolving field and so we will freely move from using recent research papers to materials from older books etc. 1 0 ] Monday, March 4: Lecture 11. 0 /Type Presentation: "On the computational complexity of deep learning", by Shai Shalev-Shwartz in 2015 Blum, Avrim L., and Ronald L. Rivest. The notes (which cover … >> 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. /Filter These are the lecture notes for FAU’s YouTube Lecture “Deep Learning”. obj Class Notes. << /DeviceRGB Deep Learning at FAU. Lecture notes. R VideoLectures Online video on RL. Part 1: Introduction to Generative Deep Learning Chapter 1. >> 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. 5.0 … Image under CC BY 4.0 from the Deep Learning Lecture. 34 /MediaBox Compose; Chapter 8. The Future of Generative Modeling; 3. This is a full transcript of the lecture video & matching slides. << R 33 Generative Modeling; Chapter 2. Older lecture notes are provided before the class for students who want to consult it before the lecture. /Annots ]���Fes�������[>�����r21 0 R Image under CC BY 4.0 from the Deep Learning Lecture. << ¶âÈ XO8=]¨dLãp×!Í$ÈÂ.SW`Ã6Ò»í«AóÖ/|ö¾ÈË{OÙPÚz³{ªfOÛí¾ºh7ÝN÷Ü01"ê¶ú6j¯}¦'T3,aü+-,/±±þÅàLGñ,_É\Ý2L³×è¾_'©R. /Transparency ] �)��w�0�*����"r�lt5Oz0���&��=��ʿQA3��E5�,I9�َK�PPۅT������숓uXJ�� I�C���.�������������&�Ǆ|!��A�Yi�. endobj /FlateDecode obj Write; Chapter 7. << >> 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(! << 1 << 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 Still, creating a book that combined accessibility, breadth, and hands-on learning wasn’t easy. Supervised Learning (ppt) Chapter 3. R /Filter R The Deep Learning Lecture Series 2020 is a collaboration between DeepMind and the UCL Centre for Artificial Intelligence. [ obj /S Deep Learning Tutorial by LISA lab, University of Montreal COURSES 1. On autoencoders: Chapter 14 of The Deep Learning textbook. Multivariate Methods (ppt) Chapter 6. 7 R 17 R More on neural networks: Chapter 6 of The Deep Learning textbook. 0 << ] However, many found the accompanying video lectures, slides, and exercises not pedagogic enough for a fresh starter. We hope, you enjoy this as much as the videos. 1 0 [ 0 /Parent We hope, you enjoy this as much as the videos. Lecture notes/slides will be uploaded during the course. /JavaScript 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 cs224n: natural language processing with deep learning lecture notes: part v language models, rnn, gru and lstm 2 called an n-gram Language Model. Machine Learning by Andrew Ng in Coursera 2. 3 Deep Learning ; 10/14 : Lecture 10 Bias - Variance. 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 This is a full transcript of the lecture video & matching slides. /Outlines >> ;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) = … /S 0 /Filter >> Deep Learning is one of the most highly sought after skills in AI. << /CS The concept of deep learning is not new. This course describes the use of neural networks in machine learning: deep learning, recurrent networks, and other supervised and unsupervised machine-learning … Y��%#^4U�Z��+��`�� �T�}x��/�(v�ޔ��O�~�r��� U+�{�9Q� ���w|�ܢ��v�e{�]�L�&�2[}O6)]cCN���79����Tr4��l�? R R R Saxe, A. M., McClelland, J. L., and Ganguli, S. (2013). << /Resources 0 Slides HW0 (coding) due (Jan 18). Deep Learning: A recent book on deep learning by leading researchers in the field. Maximum likelihood 10 Describe relationships — classical statistics; Predicting future outputs — machine learning; 2.3 Learning the model from training data. stream Download Textbook lecture notes. jF�`;`]���6B�G�K�W@C̖k��n��[�� 琂�/_�S��A�/ ���m�%�o��QDҥ 2.1 The regression problem 2.2 The linear regression model. /MediaBox 0 Due Wednesday, 10/21 at 11:59pm 10/9 : Section 4 Friday TA Lecture: Deep Learning. 1. [ endobj 2019 Edition, Kindle Edition by Wojciech Samek (Editor), Grégoire Montavon (Editor), Andrea Vedaldi (Editor), & Format: Kindle Edition. >> 0 Deep Learning by Yoshua Bengio, Ian Goodfellow and Aaron Courville 2. >> Neural Networks and Deep Learning by Michael Nielsen 3. 25 endstream >> endobj [ endobj >> /DeviceRGB 27 405 DL book: Deep Feedforward Nets; DL book: Regularization for DL; W3: Jan 22 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 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���$C 2 /Contents ML Applications need more than algorithms Learning Systems: this course. *y�:��=]�Gkדּ�t����ucn�� �$� R 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��$�vI�@(m����@BX�ro ��8ߍ-Bp&�sB��,����������^Ɯnk 0 NPTEL provides E-learning through online Web and Video courses various streams. Lecture 7: Tuesday April 28: Training Neural Networks, part I Activation functions, data processing Batch Normalization, Transfer learning Neural Nets notes 1 Neural Nets notes 2 Neural Nets notes 3 tips/tricks: , , (optional) Deep Learning [Nature] (optional) Proposal due: Monday April 27 ] /Group 0 Backpropagation. obj /CS << /Resources 709 stream Deep Learning by Microsoft Research 4. 15 /Parent R R ] Regularization. /FlateDecode 6 /DeviceRGB obj >> The Deep Learning Handbook is a project in progress to help study the Deep Learning book by Goodfellow et al.. Goodfellow's masterpiece is a vibrant and precious resource to introduce the booming topic of deep learning. R jtheaton@wustl.edu. 0 On the importance of initialization and momentum in deep learning. 1 ɗ���>���H��Sl�4 _�x{R%BH��� �v�c��|sq��܇�Z�c2 I,�&�Z-�L 4���B˟�Vd����4;j]U;͛23y%tma��d��������ۜ���egrq���/�wl�@�'�9G���7ݦ�ԝu��[wn����[��r�g$A%/�ʇS��OH�'H�h Updated notes will be available here as ppt and pdf files after the lecture. Generative Adversarial Networks; Part 2: Teaching Machines to Paint, Write, Compose and Play Chapter 5. 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. << 0 Matrix multiply as computational core of learning. Deep Learning Book: Chapters 4 and 5. obj 720 Bayesian Decision Theory (ppt) Chapter 4. /Contents 0 /PageLabels 27 7 /S /Page Deep Learning ; 10/7: Assignment: Problem Set 2 will be released. School of Engineering and Applied Science, Washington University in St. Louis, 1 Brookings. /Transparency Deep Learning Handbook. Slides ; 10/12 : Lecture 9 Neural Networks 2. /S 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. ��]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| /Names endobj >> ] /Type 405 R /Parent 0 /Type 405 endobj endobj Lecture 1: Introduction to Deep Learning CSE599W: Spring 2018. stream 2014 Lecture 2 McCulloch Pitts Neuron, Thresholding Logic, Perceptrons, Perceptron Learning Algorithm and Convergence, Multilayer Perceptrons (MLPs), Representation Power of MLPs eBBh`�Vj)��A�%���/�/�-�E�t����(��w)+�B�-�Δ���{��=�����/ɩ]2���W2P*q�{oxVH2��_�7�#���#v�vXN� �z����W�e3y�����x��W�SA��V��Ԡ� obj /Annots << 19 /Type >> 9 18 Deep neural networks. [ ] 16 Time and Location Mon Jan 27 - Fri Jan 31, 2020. 19

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