/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�
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��8ߍ-Bp&�sB��,����������^Ɯnk 24 R Explainable AI: Interpreting, Explaining and Visualizing Deep Learning (Lecture Notes in Computer Science Book 11700) 1st ed. 16 5 Lecture notes/slides will be uploaded during the course. >> /Transparency << [ endobj 720 34 The Future of Generative Modeling; 3. /Type >> 0 R Deep Learning An MIT Press book in preparation Ian Goodfellow, Yoshua Bengio and Aaron Courville. Still, creating a book that combined accessibility, breadth, and hands-on learning wasn’t easy. 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{OÙPÚz³{ªfOÛí¾ºh7ÝN÷Ü01"ê¶ú6j¯}¦'T3,aü+-,/±±þÅàLGñ,_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
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��/�]�������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
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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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