Written for undergraduate students in computer science, this book provides a comprehensive overview of the field of neural networks. The book presents readers with the application of neural networks to areas like bioinformatics, robotics, communication, image processing, and healthcare. Topics covered include fundamental models of artificial neural networks, perception networks, and adaptive resonance theory.
Introduction To Neural Networks Using Matlab 6.0 .pdfl
Neural networks enjoy widespread success in both research and industry and, with the advent of quantum technology, it is a crucial challenge to design quantum neural networks for fully quantum learning tasks. Here we propose a truly quantum analogue of classical neurons, which form quantum feedforward neural networks capable of universal quantum computation. We describe the efficient training of these networks using the fidelity as a cost function, providing both classical and efficient quantum implementations. Our method allows for fast optimisation with reduced memory requirements: the number of qudits required scales with only the width, allowing deep-network optimisation. We benchmark our proposal for the quantum task of learning an unknown unitary and find remarkable generalisation behaviour and a striking robustness to noisy training data.
In this paper we have introduced natural quantum generalisations of perceptrons and (deep) neural networks, and proposed an efficient quantum training algorithm. The resulting QML algorithm, when applied to our QNNs, demostrates remarkable capabilities, including, the ability to generalise, tolerance to noisy training data, and an absence of a barren plateau in the cost function landscape. There are many natural questions remaining in the study of QNNs including generalising the quantum perceptron definition further to cover general CP maps (thus incorporating a better model for decoherence processes), studying the effects of overfitting, and optimised implementation on the next generation of NISQ devices.
Class preparation: (video) Getting Started wth MATLAB MATLAB Introduction (skim) In-class work: pdf of MATLAB Live Script MATLAB intro zip Class 3: Tuesday, September 14 Class preparation: (videos) Edge Detection in Computer Vision Systems (read) Edge Detection in Computer Vision Systems pre-class assessment #1 (due Monday, September 13, 8:00 pm) (solutions) In-class: (recap & questions) edge detection (slides) Class 4: Wednesday, September 15 Class preparation: (review) MATLAB Introduction (conditionals, loops, user-defined functions) In-class: MATLAB programming (slides, exercises) Class 5: Friday, September 17 (lab) In-class work: MATLAB programming (cont'd) Assignment 1 work (due Wednesday, September 29) Assign1_edge_detection zip Class 6: Tuesday, September 21 Class preparation: (videos) Early Processing in the Human Visual System (read) Perception Lecture Notes: Spatial Frequency Channels, Prof. Michael Landy, NYU (optional) if you'd like to learn more about the human visual system, the Vision page on Scholarpedia provides useful articles on many of the topics covered in the videos pre-class assessment #2 (due Monday, September 20, 8:00 pm) In-class: (recap & questions) early processing in the human visual system (slides) Class 7: Wednesday, September 22 In-class: early processing in the human visual system (cont'd) introduction to stereo geometry and correspondence problem (slides) Class 8: Friday, September 24 (lab) Remote office hours for Assignment 1 work, 8:30-10:30 am Class 9: Tuesday, September 28 Class preparation: (videos) Binocular Stereo Vision (read) Wolfe et al. (2018) Sensation & Perception, Fifth Edition, p. 189-203 pre-class assessment #3 (due Monday, September 27, 8:00pm) In-class: (recap & questions) stereo geometry, stereo correspondence, region-based correspondence method (slides) Class 10: Wednesday, September 29 Assignment 1 due In-class: human stereo vision (slides)
Class 11: Friday, October 1 (lab) In-class work: stereo vision (cont'd) start Assignment 2 (due Wednesday, October 13) stereo zip Class 12: Tuesday, October 5 Class preparation: (videos) Stereo Processing in the Human Visual System (note: material from the first video on Human Stereo Vision was already discussed in class) (video) New 3D vision discovered in praying mantises (read) Williford, J. R. & von der Heydt, R. (2013) Border-ownership coding, Scholarpedia, 8(10):30040 pre-class assessment #4 (due Monday, October 4, 8:00pm) In-class: (recap & questions) human stereo vision, MPG multi-resolution stereo algorithm, border ownership (slides #1, slides #2)) Class 13: Wednesday, October 6 In-class: introduction to motion analysis, computation of the velocity field (slides) Class 14: Friday, October 8 (lab) In-class work: Assignment 2 work (cont'd) Class 15: Wednesday, October 13 Assignment 2 due Because of Fall Break, you do not need to complete the videos and reading before this class. Instead, there is a post-assessment this week, due Thursday evening. Class preparation: (videos) Visual Motion Analysis (read) online text, Introduction to Linear Algebra, Chapter 2 on Basic Vector Operations post-class assessment #5 (due Thursday, October 14, 8:00 pm) In-class: motion measurement, human motion processing (slides) Class 16: Friday, October 15 (lab) In-class work: (recap & questions) motion analysis start Assignment 3 (due Friday, October 29) motion zip Class 17: Tuesday, October 19 Class preparation: (videos) The Analysis of Observer Motion pre-class assessment #6 (due Monday, October 18, 8:00 pm) In-class: (recap & questions) 3D structure and observer movement from image motion (slides) Class 18: Wednesday, October 20 In-class: 3D structure and observer movement from image motion (cont'd) introduction to Problem 3 of Assignment 3 (simulation of observer motion) Class 19: Friday, October 22 (lab) In-class work: review of code for Problem 1 of Assignment 3 (getVideoImages.m, codeTips.m) Assignment 3 work (cont'd)
Class 20: Tuesday, October 26 Class preparation: (videos) Introduction to Face Recognition (read) A gentle introduction to deep learning for face recognition, a very light overview of the history of face recognition, with key references (optional reading) Scholarpedia article on Eigenfaces, which includes the mathematical details pre-class assessment #7 (due Monday, October 25, 8:00 pm) In-class: (recap & questions) face recognition, early computer recognition methods based on geometric features and Eigenfaces (slides) Class 21: Wednesday, October 27 In-class: face recognition, early computer recognition method based on Eigenfaces (cont'd) introduction to neural networks (slides) Class 22: Friday, October 29 (lab) Assignment 3 due In-class work: face recognition: early computer recognition method based on Eigenfaces Assignment 4, Part 1 (due Friday, November 5) eigenfaces zip Class 23: Tuesday, November 2 Class preparation: (videos) YouTube videos introducing neural networks, by Grant Sanderson (read) Using neural nets to recognize handwritten digits, Chapter 1 of free online book, Neural Networks and Deep Learning, by Michael Nielson (read up to the start of the section on "Implementing our network to classify digits") pre-class assessment #8 (due Monday, November 1, 8:00 pm) In-class: overview of final project/paper (recap & questions) introduction to neural nets (slides) Class 24: Wednesday, November 3 In-class: introduction to convolutional neural nets (slides) Class 25: Friday, November 5 (lab) Assignment 4, Part 1 due In-class work: neural nets Assignment 4, Part 2 (due Friday, November 12) Assignment 4, Part 3 (due Friday, November 12) NeuralNets zip Class 26: Tuesday, November 9 Class preparation: (video) Convolutional Neural Networks for Computer Vision by Alexander Amini (from MIT course 6.S191) (37:20) (read) Simple Introduction to Convolutional Neural Networks by Matthew Stewart no pre-class assessment this week In-class: (recap & questions) deep learning and convolutional neural networks (slides) Class 27: Wednesday, November 10 In-class: neural processing of faces (slides) Class 28: Friday, November 12 (lab) Assignment 4, Parts 2 and 3 due In-class work: analysis of fMRI data Assignment 5, Part 1 work (due Tuesday, November 23) fMRI zip Class 29: Tuesday, November 16 Class preparation: prepare for class discussion on applications of face recognition technology In-class: discussion: applications of face recognition technology and society Class 30: Wednesday, November 17 Class preparation: (video) What you can learn from studying behavior, Nancy Kanwisher (24:57) (optional video) On the neural machinery of faces, Winrich Freiwald (1:03:18) (read/skim) Face recognition by humans: Nineteen results all computer vision researchers should know, Sinha, Balas, Postrovsky & Russell pre-class assessment #9 (due Tuesday, November 16, 8:00 pm) In-class: (recap & questions) face recognition in the human visual system (slides) Viola-Jones face detection algorithm (slides) Class 31: Friday, November 19 (lab) No class today - times available for final project meetings Class 32: Tuesday, November 23 Assignment 5, Part 1 due In-class: Viola-Jones face detection algorithm Class 33: Tuesday, November 30 Remote class: zoom link: meeting ID: 977 9713 9423 what makes an image memorable, and its connection to scene understanding (slides) Class 34: Wednesday, December 1 Remote class: zoom link: meeting ID: 954 5449 0164 introduction to color vision (slides) Class 35: Friday, December 3 (lab) No class today - times available for final project meetings Class 36: Tuesday, December 7 In-class: color vision cont'd (slides) learning visual concepts in an unsupervised way (slides) Class 37: Wednesday, December 8 Student presentations Class 38: Friday, December 10 (lab) Student presentations Class 39: Tuesday, December 14 Student presentations ( c ) 2008. All Rights Reserved. Designed by Free CSS Templates.
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