/Parent /MediaBox These are the lecture notes for FAU’s YouTube Lecture “Deep Learning”. 0 obj endobj 27 1 Class Notes Sep 2 : No class Lecture 3: Sep 4: Probability Distributions : Reading: Bishop: Chapter 2, sec. 1 0 7 Y��%#^4U�Z��+��`�� �T�}x��/�(v�ޔ��O�~�r��� U+�{�9Q� ���w|�ܢ��v�e{�]�L�&�2[}O6)]cCN���79����Tr4��l�? 25 25 endobj R This book provides a solid deep learning & Jeff Heaton. Compose; Chapter 8. /JavaScript endobj Slides, Supervised Learning notes, k-NN notes: W2: Jan 15: Linear Classifiers, Loss Functions (guest lecture by Peter Anderson). We currently offer slides for only some chapters. /S eBBh`�Vj)��A�%���/�/�-�E�t����(��w)+�B�-�Δ���{��=�����/ɩ]2���W2P*q�{oxVH2��_�7�#���#v�vXN� �z����W�e3y�����x��W�SA��V��Ԡ� >> 0 Slides HW0 (coding) due (Jan 18). << 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%]. /Contents /Length 0 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. Deep Learning Book: Chapters 4 and 5. << 0 0 Deep Learning at FAU. 405 In ICLR. 0 0 /Annots 28 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. endobj 720 0 NPTEL provides E-learning through online Web and Video courses various streams. DL book: Deep Feedforward Nets; DL book: Regularization for DL; W3: Jan 22 1 This is a full transcript of the lecture video & matching slides. Bayesian Decision Theory (ppt) Chapter 4. /CS R stream R 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. R obj /Filter /S /Group Monday, March 4: Lecture 11. Class Notes. >> /St << 0 /D 33 The notes (which cover … /Resources Variational Autoencoders; Chapter 4. /S We hope, you enjoy this as much as the videos. R endobj endobj 0 >> obj Deep Learning at FAU. /FlateDecode 0 0 obj 8 Lecturers. 0 �)��w�0�*����"r�lt5Oz0���&��=��ʿQA3��E5�,I9�َK�PPۅT������숓uXJ�� I�C���.�������������&�DŽ|!��A�Yi�. Image under CC BY 4.0 from the Deep Learning Lecture. 0 The book can be downloaded from the link for academic purpose. Updated notes will be available here as ppt and pdf files after the lecture. ] VideoLectures Online video on RL. >> [ /MediaBox Write; Chapter 7. In Proceedings of the 30th international conference on machine learning (ICML-13) (pp. /Pages 4 In deep learning, we don’t need to explicitly program everything. 0 2 ��]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| Deep Learning: A recent book on deep learning by leading researchers in the field. 0 [ 0 0 R >> /DeviceRGB /DeviceRGB 15 9 Deep neural networks. Backpropagation. Presentation: "On the computational complexity of deep learning", by Shai Shalev-Shwartz in 2015 Blum, Avrim L., and Ronald L. 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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 This is a full transcript of the lecture video & matching slides. Describe relationships — classical statistics; Predicting future outputs — machine learning; 2.3 Learning the model from training data. ... Introduction (ppt) Chapter 2. Generative Adversarial Networks; Part 2: Teaching Machines to Paint, Write, Compose and Play Chapter 5. << /Contents 36 << ¶âÈ XO8=]¨›dLãp—“×!Í$ÈÂ.SW`Ã6Ò»í«AóÖ/|ö¾ÈË{ƒO€ÙPÚz³{ªfOÛí¾ºh7ÝN÷Ü01"Œê¶ú6j¯}¦'Tƒ3,a‹ü+-,/±±þÅàŽLGñ,€_É\Ý2L³×è¾_'©R. obj endstream >> >> 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 >> obj 1. Deep Learning ; 10/14 : Lecture 10 Bias - Variance. endobj Matrix multiply as computational core of learning. 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 /Type Saxe, A. M., McClelland, J. L., and Ganguli, S. (2013). stream /CS ] Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. The Deep Learning Lecture Series 2020 is a collaboration between DeepMind and the UCL Centre for Artificial Intelligence. Lecture notes. 1.3 Overview of these lecture notes 1.4 Further reading 2 The regression problem and linear regression 11. 534 << stream Lecture notes will be uploaded a few days after most lectures. ML Applications need more than algorithms Learning Systems: this course. 3 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. 0 Maximum likelihood obj /Length /Transparency obj 0 0 obj Not all topics in the book will be covered in class. /Catalog Image under CC BY 4.0 from the Deep Learning Lecture. obj Play; Chapter 9. R Generative Modeling; Chapter 2. 18 << Time and Location Mon Jan 27 - Fri Jan 31, 2020. /Type /FlateDecode /Page R 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. R Regularization. /Filter /Nums endobj Deep learning is a rapidly evolving field and so we will freely move from using recent research papers to materials from older books etc. 1139-1147). /Annots /Group Juergen Schmidhuber, Deep Learning in Neural Networks: An Overview. 0 /FlateDecode 10707 (Spring 2019): Deep Learning - Lecture Schedule Tentative Lecture Schedule. 33 ] Older lecture notes are provided before the class for students who want to consult it before the lecture. /Length 0 ��������Ԍ�A�L�9���S�y�c=/� cs224n: natural language processing with deep learning lecture notes: part v language models, rnn, gru and lstm 2 called an n-gram Language Model. /CS 0 /Group 0 >> 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 17 Deep Learning Tutorial by LISA lab, University of Montreal COURSES 1. R /CS We hope, you enjoy this as much as the videos. [ /DeviceRGB 720 >> << /Resources 2019 Edition, Kindle Edition by Wojciech Samek (Editor), Grégoire Montavon (Editor), Andrea Vedaldi (Editor), & Format: Kindle Edition. 28 >> 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� 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. /Annots These are the lecture notes for FAU’s YouTube Lecture “Deep Learning”. 6 Machine Learning by Andrew Ng in Coursera 2. 0 2.1-2.4 Deep Learning Book: Chapter 3 Class Notes Lecture 4: Sep 9: Neural Networks I : Reading: Bishop, Chapter 5: sec. 405 /MediaBox However, many found the accompanying video lectures, slides, and exercises not pedagogic enough for a fresh starter. We plan to offer lecture slides accompanying all chapters of this book. 405 0 << /Contents 5.0 … 0 /Creator 0 10 0 32 Sep 14/16, Machine Learning: Introduction to Machine Learning, Regression. R 709 /Transparency ] /Parent 473 /Resources Paint; Chapter 6. 0 /Transparency /Parent School of Engineering and Applied Science, Washington University in St. Louis, 1 Brookings. obj Neural Networks and Deep Learning by Michael Nielsen 3. Part 1: Introduction to Generative Deep Learning Chapter 1. The concept of deep learning is not new. Deep Learning. 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���$C Background reading material: On neural networks: Chapter 20 of Understanding Machine Learning: From Theory to Algorithms. 0 /FlateDecode ] R /Annots %PDF-1.4 ] R [ This course describes the use of neural networks in machine learning: deep learning, recurrent networks, and other supervised and unsupervised machine-learning … [ ] 0 R [ << Deep Learning algorithms aim to learn feature hierarchies with features at higher levels in the hierarchy formed by the composition of lower level features. endstream R 0 << /Contents /Resources 18 ... Books and Resources. endobj 0 /PageLabels 0 >> 0 /Filter 35 /Type 27 On autoencoders: Chapter 14 of The Deep Learning textbook. 16 << ;b) = 1 m Xm i=1 L(^y(i);y(i)) = 1 m Xm i=1 y(i) log ^y(i) + (1 h(i))log(1 ^y(i)) 1.3.4 Gradient Descent Recall the estimator ^y= ˙(!Tx+b), and sigmoid function ˙(z) = … Deep Learning ; 10/7: Assignment: Problem Set 2 will be released. obj Deep Learning by Yoshua Bengio, Ian Goodfellow and Aaron Courville 2. Deep Learning Handbook. << Book Exercises External Links Lectures. Deep Learning by Microsoft Research 4. Deep Learning; Chapter 3. endobj ] 2.1 The regression problem 2.2 The linear regression model. 0 /Outlines 405 obj R *y�:��=]�Gkדּ�t����ucn�� �$� 19 0 2014 Lecture 2 McCulloch Pitts Neuron, Thresholding Logic, Perceptrons, Perceptron Learning Algorithm and Convergence, Multilayer Perceptrons (MLPs), Representation Power of MLPs Slides ; 10/12 : Lecture 9 Neural Networks 2. endobj endobj /S Due Wednesday, 10/21 at 11:59pm 10/9 : Section 4 Friday TA Lecture: Deep Learning. /S 0 10 /Parent 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 19 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 >> 26 Multivariate Methods (ppt) Chapter 6. R 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 1 stream Class Notes. /Filter /MediaBox jF�`;`]���6B�G�K�W@C̖k��n��[�� 琂�/_�S��A�/ ���m�%�o��QDҥ 9 R /Page 0 0 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(! /Names obj >> /Group % ���� Download Textbook lecture notes. [ obj /Length 1 On the importance of initialization and momentum in deep learning. endobj << More on neural networks: Chapter 6 of The Deep Learning textbook. 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 0 << 720 >> /Type endstream /DeviceRGB 0 /Page (�� G o o g l e) The 12 video lectures cover topics from neural network foundations and optimisation through to generative adversarial networks and responsible innovation. R Lecture 1: Introduction to Deep Learning CSE599W: Spring 2018. Supervised Learning (ppt) Chapter 3. 7 These are lecture notes for my course on Artificial Neural Networks that I have given at Chalmers and Gothenburg University. jtheaton@wustl.edu. Parametric Methods (ppt) 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. ]���Fes�������[>�����r21 Slides: W2: Jan 17: Regularization, Neural Networks. endobj obj With the advent of Deep Learning new models of unsupervised learning of features for Time-series analysis and forecast have been developed. Deep Learning is one of the most highly sought after skills in AI. /Page DEEP LEARNING LIBRARY FREE ONLINE BOOKS 1. 1
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