DOCSLIB.ORG

Reorienting Machine Learning Education Towards Tinkerers and ML-Engaged Citizens Natalie

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Reorienting Machine Learning Education Towards Tinkerers and ML-Engaged Citizens Natalie Reorienting Machine Learning Education Towards Tinkerers and ML-Engaged Citizens by Natalie Lao S.B., Massachusetts Institute of Technology (2016) M.Eng., Massachusetts Institute of Technology (2017) Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Electrical Engineering and Computer Science at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY September 2020 ○c Massachusetts Institute of Technology 2020. All rights reserved. Author................................................................ Department of Electrical Engineering and Computer Science August 28, 2020 Certified by. Harold (Hal) Abelson Class of 1922 Professor of Electrical Engineering and Computer Science Thesis Supervisor Accepted by . Leslie A. Kolodziejski Professor of Electrical Engineering and Computer Science Chair, Department Committee on Graduate Students 2 Reorienting Machine Learning Education Towards Tinkerers and ML-Engaged Citizens by Natalie Lao Submitted to the Department of Electrical Engineering and Computer Science on August 28, 2020, in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Electrical Engineering and Computer Science Abstract Artificial intelligence (AI) and machine learning (ML) technologies are appearing in everyday contexts, from recommendation systems for shopping websites to self- driving cars. As researchers and engineers develop and apply these technologies to make consequential decisions in everyone’s lives, it is crucial that the public under- stands the AI-powered world, can discuss AI policies, and become empowered to engage in shaping these technologies’ futures. Historically, ML application devel- opment has been accessible only to those with strong computational backgrounds working or conducting research in highly technical fields. As ML models become more powerful and computing hardware becomes faster and cheaper, it is now tech- nologically possible for anyone with a laptop to learn about and tinker with ML. This includes audiences without programming or advanced mathematics knowledge, who can use public ML tools to construct both useful technical artifacts and meaningful socio-ethical artifacts around ML. My research investigates the following question: How do we reorient ML engineering education to transform “ML consumers” to be “ML contributors”? In order to illuminate this problem, I have created a Machine Learning Education Framework. In this dissertation, I present the framework and three applications of it: (1) a course based on the framework that aims to develop ML self-efficacy in general college-level audiences; (2) a curriculum rubric basedon the ML Education Framework and analyses of 6 ML/AI courses using the rubric; and (3) two public ML tools for high school and above ML tinkerers that empower them to create personal ML applications through image and audio classification. The ML Education Framework is organized into three interconnected categories: Knowledge (General ML Knowledge, Knowledge of ML Methods, Bias in ML Systems, Societal Implications of ML), Skills (ML Problem Scoping, ML Project Planning, Creating ML Artifacts, Analysis of ML Design Intentions & Results, ML Advocacy, Independent Out-of-Class Learning), and Attitudes (Interest, Identity & Community, Self-Efficacy, Persistence). The three top level categories of the framework werecho- sen to encompass the holistic process of teaching people ML and ensuring continued participation in the field. “Knowledge” about ML will be necessary for individuals to 3 thrive in today’s ML-driven world and builds foundations for further engagement with the technology. ML “Skills” define specific actions and activities that students need to do and practice in order to become ML contributors. The category "Attitudes" reflects long-term societal goals for educational initiatives in ML, and addresses the perspectives that students should develop through an ML course. This framework can be used in creating new ML curricula, in analyzing the results of ML interven- tions, and in situating support tools within ML education for students of a variety of backgrounds and levels. Today a large portion of the world solely resides in the “ML consumer” population. Regulation of ML technologies is nascent. As policymakers think about reasonable legal structures, the public should be able to actively participate in these discussions. It is imperative that we empower these “ML consumers” to become “ML tinkerers” and “ML-engaged citizens”. Thesis Supervisor: Harold (Hal) Abelson Title: Class of 1922 Professor of Electrical Engineering and Computer Science 4 Acknowledgments I have received a lot of help and support throughout the past 3 years of my PhD at MIT. First, I want to thank my thesis committee: Hal Abelson, Anantha Chan- drakasan, and Eric Klopfer. Thank you for your mentorship and support throughout this whole process. Hal, I’ve taken your classes and worked on research with you since my junior year of undergrad at MIT. Seeing your pioneering approach over the past years has really inspired me to work in academia and do research related to democra- tizing technology and education. Anantha, I’ve also known you since my undergrad years at MIT. You’ve always pushed me to think more deeply when it comes to re- search, and even though you’re incredibly busy as dean, your thoughtful feedback has helped me consider how to maximize the real-world impact of my work. Eric, your expertise in education has been invaluable to the quality and rigor of this work. Your advice has really helped me refine my Machine Learning Education Framework and the corresponding rubrics into something that hopefully can be used by more people. I also want to thank the people who were instrumental in the papers I’ve written during my PhD. Irene Lee, the 6.S198 staff, and my 6.S198 students helped make the Deep Learning Practicum course a wonderful experience. The Google PAIR and TensorFlow.JS teams were also extremely supportive during this process: I recall we were using deeplearn.js when it first came out, so sometimes we would run into bugs while making the course materials. The folks at Google would help us fix these issues within a few days. I would also like to acknowledge the staff and students from the Internet Policy Research Initiative and the MIT App Inventor team for helping create PIC and PAC, and running our high school workshops. Special shout-outs to Danny Tang, Yuria Utsumi, Nikhil Bhatia, Evan Patton, Jeff Schiller, Karen Lang, Selim Tezel, and Marisol Diaz. Also a big thank you to Ingrid Roche at the Boston Latin Academy for letting us run workshops in her classes. I had many meaningful and rewarding conversations with people in the ML and AI education community when creating my framework and doing my evaluations, and these conversations were instrumental to my research process. Thank you for taking 5 the time to speak with me: Richard Jung and Nicholas Luu from Technovation, Sandhya Simhan and Ortal Arel from deeplearning.ai, Blakeley Payne and Randi Williams from the MIT Media Lab, and Teemu Roos from the University of Helsinki. Finally, I would like to thank my friends, my parents, my fellow graduate students, my fiancé Nicholas Kwok, and my dog Sophie for all of their support throughout these years. MIT and learning how to do research would have been less fun without you all. 6 Contents 1 Introduction 19 1.1 Vision: Everyone can understand and tinker with machine learning . 21 1.2 Photography: An Analogy for Democratizing Machine Learning . 25 1.3 A Brief History of AI . 29 1.4 Defining Artificial Intelligence, Machine Learning, and Deep Learning 32 2 Background: ML Education and Educational Theories as Applied to Teaching ML 33 2.1 The Current State of Machine Learning Education . 33 2.1.1 Technical ML Courses . 34 2.1.2 General Adult ML Courses . 35 2.1.3 K-12 ML Courses . 36 2.2 Education Theories as Applied to ML and AI Education . 43 2.2.1 Self-Efficacy . 44 2.2.2 Constructionism, Computational Action, and Computational Identity . 45 2.2.3 Experiential Learning Approaches as Applied to ML . 47 2.3 Similar Educational Frameworks in Computing and Engineering . 49 2.3.1 Computational Thinking . 51 2.3.2 Conceiving - Designing - Implementing - Operating (CDIO) and Use-Modify-Create (UMC) . 53 3 The Machine Learning Education Framework 57 7 3.1 The Machine Learning Education Framework . 58 3.1.1 Knowledge . 58 3.1.2 Skills . 59 3.1.3 Attitudes . 61 3.1.4 Essential Items of the Framework . 61 3.2 Interconnections within the Framework . 62 4 6.S198: Designing a College-Level Actionable Machine Learning Practicum 67 4.1 Instruments and Assessment . 68 4.2 Application of Educational Theories and the ML Education Framework 69 4.2.1 Experiential learning and Use-Modify-Create in Deep Learning Practicum . 69 4.2.2 Self-Efficacy and Computational Action in Deep Learning Practicum 72 4.2.3 Course Intentions within the ML Education Framework . 74 4.3 Transfer Learning for Empowering Beginners in ML Application Creation 76 4.4 Deep Learning Practicum Version 1 . 76 4.4.1 Student Demographics . 77 4.4.2 Teaching Staff and Industry Mentors . 79 4.4.3 Capstone Projects . 79 4.4.4 Learnings from Version 1 that Informed Version 2 . 80 4.5 Deep Learning Practicum Version 2 . 81 4.5.1 Changes from Version 1 . 81 4.5.2 Student Demographics . 83 4.5.3 Capstone Projects . 83 4.6 Findings and Analysis . 84 4.6.1 Project Themes and Approaches . 84 4.6.2 Self-Efficacy . 94 4.6.3 Mastery of ML Methods . 100 4.6.4 Mastery of ML Artifact Creation Skills . 102 8 4.6.5 Identity and Perceptions . 105 4.7 Discussion . 106 5 An Analysis of Curricula Using the ML Education Framework 111 5.1 ML Education Framework: Curriculum Rubric . 111 5.2 Analysis of 6 ML and AI Curricula Using the Curriculum Rubric .
Load more

Recommended publications
  • Getting Started with Machine Learning
    Getting Started with Machine Learning CSC131 The Beauty & Joy of Computing Cornell College 600 First Street SW Mount Vernon, Iowa 52314 September 2018 ii Contents 1 Applications: where machine learning is helping1 1.1 Sheldon Branch............................1 1.2 Bram Dedrick.............................4 1.3 Tony Ferenzi.............................5 1.3.1 Benefits of Machine Learning................5 1.4 William Golden............................7 1.4.1 Humans: The Teachers of Technology...........7 1.5 Yuan Hong..............................9 1.6 Easton Jensen............................. 11 1.7 Rodrigo Martinez........................... 13 1.7.1 Machine Learning in Medicine............... 13 1.8 Matt Morrical............................. 15 1.9 Ella Nelson.............................. 16 1.10 Koichi Okazaki............................ 17 1.11 Jakob Orel.............................. 19 1.12 Marcellus Parks............................ 20 1.13 Lydia Sanchez............................. 22 1.14 Tiff Serra-Pichardo.......................... 24 1.15 Austin Stala.............................. 25 1.16 Nicole Trenholm........................... 26 1.17 Maddy Weaver............................ 28 1.18 Peter Weber.............................. 29 iii iv CONTENTS 2 Recommendations: How to learn more about machine learning 31 2.1 Sheldon Branch............................ 31 2.1.1 Course 1: Machine Learning................. 31 2.1.2 Course 2: Robotics: Vision Intelligence and Machine Learn- ing............................... 33 2.1.3 Course
    [Show full text]
  • Mlops: from Model-Centric to Data-Centric AI
    MLOps: From Model-centric to Data-centric AI Andrew Ng AI system = Code + Data (model/algorithm) Andrew Ng Inspecting steel sheets for defects Examples of defects Baseline system: 76.2% accuracy Target: 90.0% accuracy Andrew Ng Audience poll: Should the team improve the code or the data? Poll results: Andrew Ng Improving the code vs. the data Steel defect Solar Surface detection panel inspection Baseline 76.2% 75.68% 85.05% Model-centric +0% +0.04% +0.00% (76.2%) (75.72%) (85.05%) Data-centric +16.9% +3.06% +0.4% (93.1%) (78.74%) (85.45%) Andrew Ng Data is Food for AI ~1% of AI research? ~99% of AI research? 80% 20% PREP ACTION Source and prepare high quality ingredients Cook a meal Source and prepare high quality data Train a model Andrew Ng Lifecycle of an ML Project Scope Collect Train Deploy in project data model production Define project Define and Training, error Deploy, monitor collect data analysis & iterative and maintain improvement system Andrew Ng Scoping: Speech Recognition Scope Collect Train Deploy in project data model production Define project Decide to work on speech recognition for voice search Andrew Ng Collect Data: Speech Recognition Scope Collect Train Deploy in project data model production Define and collect data “Um, today’s weather” Is the data labeled consistently? “Um… today’s weather” “Today’s weather” Andrew Ng Iguana Detection Example Labeling instruction: Use bounding boxes to indicate the position of iguanas Andrew Ng Making data quality systematic: MLOps • Ask two independent labelers to label a sample of images.
    [Show full text]
  • Deep Learning I: Gradient Descent
    Roadmap Intro, model, cost Gradient descent Deep Learning I: Gradient Descent Hinrich Sch¨utze Center for Information and Language Processing, LMU Munich 2017-07-19 Sch¨utze (LMU Munich): Gradient descent 1 / 40 Roadmap Intro, model, cost Gradient descent Overview 1 Roadmap 2 Intro, model, cost 3 Gradient descent Sch¨utze (LMU Munich): Gradient descent 2 / 40 Roadmap Intro, model, cost Gradient descent Outline 1 Roadmap 2 Intro, model, cost 3 Gradient descent Sch¨utze (LMU Munich): Gradient descent 3 / 40 Roadmap Intro, model, cost Gradient descent word2vec skipgram predict, based on input word, a context word Sch¨utze (LMU Munich): Gradient descent 4 / 40 Roadmap Intro, model, cost Gradient descent word2vec skipgram predict, based on input word, a context word Sch¨utze (LMU Munich): Gradient descent 5 / 40 Roadmap Intro, model, cost Gradient descent word2vec parameter estimation: Historical development vs. presentation in this lecture Mikolov et al. (2013) introduce word2vec, estimating parameters by gradient descent. (today) Still the learning algorithm used by default and in most cases Levy and Goldberg (2014) show near-equivalence to a particular type of matrix factorization. (yesterday) Important because it links two important bodies of research: neural networks and distributional semantics Sch¨utze (LMU Munich): Gradient descent 6 / 40 Roadmap Intro, model, cost Gradient descent Gradient descent (GD) Gradient descent is a learning algorithm. Given: a hypothesis space (or model family) an objective function or cost function a training set Gradient descent (GD) finds a set of parameters, i.e., a member of the hypothesis space (or specified model) that performs well on the objective for the training set.
    [Show full text]
  • Face Recognition in Unconstrained Conditions: a Systematic Review
    Face Recognition in Unconstrained Conditions: A Systematic Review ANDREW JASON SHEPLEY, Charles Darwin University, Australia ABSTRACT Face recognition is a biometric which is attracting significant research, commercial and government interest, as it provides a discreet, non-intrusive way of detecting, and recognizing individuals, without need for the subject’s knowledge or consent. This is due to reduced cost, and evolution in hardware and algorithms which have improved their ability to handle unconstrained conditions. Evidently affordable and efficient applications are required. However, there is much debate over which methods are most appropriate, particularly in the context of the growing importance of deep neural network-based face recognition systems. This systematic review attempts to provide clarity on both issues by organizing the plethora of research and data in this field to clarify current research trends, state-of-the-art methods, and provides an outline of their benefits and shortcomings. Overall, this research covered 1,330 relevant studies, showing an increase of over 200% in research interest in the field of face recognition over the past 6 years. Our results also demonstrated that deep learning methods are the prime focus of modern research due to improvements in hardware databases and increasing understanding of neural networks. In contrast, traditional methods have lost favor amongst researchers due to their inherent limitations in accuracy, and lack of efficiency when handling large amounts of data. Keywords: unconstrained face recognition, deep neural networks, feature extraction, face databases, traditional handcrafted features 1 INTRODUCTION The development of accurate and efficient face recognition systems for use in unconstrained conditions is an area of high research interest.
    [Show full text]
  • Deep Learning: State of the Art (2020) Deep Learning Lecture Series
    Deep Learning: State of the Art (2020) Deep Learning Lecture Series For the full list of references visit: http://bit.ly/deeplearn-sota-2020 https://deeplearning.mit.edu 2020 Outline • Deep Learning Growth, Celebrations, and Limitations • Deep Learning and Deep RL Frameworks • Natural Language Processing • Deep RL and Self-Play • Science of Deep Learning and Interesting Directions • Autonomous Vehicles and AI-Assisted Driving • Government, Politics, Policy • Courses, Tutorials, Books • General Hopes for 2020 For the full list of references visit: http://bit.ly/deeplearn-sota-2020 https://deeplearning.mit.edu 2020 “AI began with an ancient wish to forge the gods.” - Pamela McCorduck, Machines Who Think, 1979 Frankenstein (1818) Ex Machina (2015) Visualized here are 3% of the neurons and 0.0001% of the synapses in the brain. Thalamocortical system visualization via DigiCortex Engine. For the full list of references visit: http://bit.ly/deeplearn-sota-2020 https://deeplearning.mit.edu 2020 Deep Learning & AI in Context of Human History We are here Perspective: • Universe created 13.8 billion years ago • Earth created 4.54 billion years ago • Modern humans 300,000 years ago 1700s and beyond: Industrial revolution, steam • Civilization engine, mechanized factory systems, machine tools 12,000 years ago • Written record 5,000 years ago For the full list of references visit: http://bit.ly/deeplearn-sota-2020 https://deeplearning.mit.edu 2020 Artificial Intelligence in Context of Human History We are here Perspective: • Universe created 13.8 billion years ago • Earth created 4.54 billion years ago • Modern humans Dreams, mathematical foundations, and engineering in reality. 300,000 years ago Alan Turing, 1951: “It seems probable that once the machine • Civilization thinking method had started, it would not take long to outstrip 12,000 years ago our feeble powers.
    [Show full text]
  • One Hidden Layer Neural Network Neural Networks Overview
    One hidden layer Neural Network Neural Networks deeplearning.ai Overview What is a Neural Network? !" !# %& !$ x w ' = )*! + , - = .(') ℒ(-, %) b !" !# %& !$ x ["] ["] ["] ["] ["] ["] [#] [#] ["] [#] [#] [#] [#] 4 ' = 4 ! + , - = .(' ) ' = 4 - + , - = .(' ) ℒ(- , %) ,["] 4[#] ,[#] Andrew Ng One hidden layer Neural Network Neural Network deeplearning.ai Representation Neural Network Representation !" !# %& !$ Andrew Ng One hidden layer Neural Network Computing a deeplearning.ai Neural Network’s Output Neural Network Representation !" !" !# )!! + + -(') , = %& !# %& ' , !$ !$ ' = )!! + + , = -(') Andrew Ng Neural Network Representation $( $( $) #!$ + & +(!) ' = ./ $) ./ ! ' $* $* ! ! = # $ + & $( ' = +(!) $) ./ $* Andrew Ng Neural Network Representation " " " , ["] ["] " '" )" = +" ! + /" , ' " = 3()" ) !" " " " , ["] ["] " '# )# = +# ! + /# , ' # = 3()# ) ! %& # " " " , ["] ["] " '$ )$ = +$ ! + /$ , ' $ = 3()$ ) !$ " " " , ["] ["] " '( )( = +( ! + /( , ' ( = 3()( ) Andrew Ng Neural Network Representation learning ( " " Given input x: %" ( " , ! " = $ " % + ' " % ./ , " (- " " %- ( = )(! ) " (0 ! , = $ , ( " + ' , ( , = )(! , ) Andrew Ng One hidden layer Neural Network Vectorizing across deeplearning.ai multiple examples Vectorizing across multiple examples ' " = ) " ! + + " !" , " = -(' " ) %& !# ' # = ) # , " + + # ! $ , # = -(' # ) Andrew Ng Vectorizing across multiple examples for i = 1 to m: ! " ($) = ' " (($) + * " + " ($) = ,(! " $ ) ! - ($) = ' - + " ($) + * - + - ($) = ,(! - $ ) Andrew Ng One hidden layer Neural Network Explanation
    [Show full text]
  • Where We're At
    Where We’re At Progress of AI and Related Technologies How Big is the Field of AI? ● +50% publications/5 years ● 106 journals, 7,125 organizations ● Approx $56M funding from NSF in 2011, but ● Most funding is from private companies and hard to tally ● Large influx of corporate funding recently Deep Learning Causal Networks Geoffrey E. Hinton, Simon Pearl, Judea. Causality: Models, Osindero, Yee-Whye Teh: A fast Reasoning and Inference (2000) algorithm for deep belief nets (2006) Recent Progress DeepMind ● Combined deep learning (convolutional neural net) with reinforcement learning to play 7 Atari games ● Sold to Google for >$400M in Jan 2014 Demis Hassabis required an “AI ethics board” as a condition of sale ● Demis says: 50% chance of AGI within 15yrs Google ● Commercially deployed: language translation, speech recognition, OCR, image classification ● Massive software and hardware infrastructure for large- scale computation ● Large datasets: Web, Books, Scholar, ReCaptcha, YouTube ● Prominent researchers: Ray Kurzweil, Peter Norvig, Andrew Ng, Geoffrey Hinton, Jeff Dean ● In 2013, bought: DeepMind, DNNResearch, 8 Robotics companies Other Groups Working on AI IBM Watson Research Center Beat the world champion at Jeopardy (2011) Located in Cambridge Facebook New lab headed by Yann LeCun (famous for: backpropagation algorithm, convolutional neural nets), announced Dec 2013 Human Brain Project €1.1B in EU funding, aimed at simulating complete human brain on supercomputers Other Groups Working on AI Blue Brain Project Began 2005, simulated
    [Show full text]
  • Notes on Andrew Ng's CS 229 Machine Learning Course
    Notes on Andrew Ng’s CS 229 Machine Learning Course Tyler Neylon 331.2016 These are notes I’m taking as I review material from Andrew Ng’s CS 229 course on machine learning. Specifically, I’m watching these videos and looking at the written notes and assignments posted here. These notes are available in two formats: html and pdf. I’ll organize these notes to correspond with the written notes from the class. As I write these notes, I’m also putting together some homework solutions. 1 On lecture notes 1 The notes in this section are based on lecture notes 1. 1.1 Gradient descent in general Given a cost function J(θ), the general form of an update is ∂J θj := θj − α . ∂θj It bothers me that α is an arbitrary parameter. What is the best way to choose this parameter? Intuitively, it could be chosen based on some estimate or actual value of the second derivative of J. What can be theoretically guaranteed about the rate of convergence under appropriate conditions? Why not use Newton’s method? A general guess: the second derivative of J becomes cumbersome to work with. It seems worthwhile to keep my eye open for opportunities to apply improved optimization algorithms in specific cases. 1 1.2 Gradient descent on linear regression I realize this is a toy problem because linear regression in practice is not solve iteratively, but it seems worth understanding well. The general update equation is, for a single example i, (i) (i) (i) θj := θj + α(y − hθ(x ))xj .
    [Show full text]
  • Ignite Your AI Curiosity with Dr. Andrew Ng Will AI Penetrate Every Industry?
    AI Ignition Ignite your AI curiosity with Dr. Andrew Ng Will AI penetrate every industry? From Deloitte’s AI Institute, this is AI Ignition. A monthly chat about the human side of artificial intelligence, with your host, Beena Ammanath. We’ll take a deep dive into the past, present, and future of AI, machine learning, neural networks, and other cutting-edge technologies. Here is your host, Beena. Beena Ammanath (Beena): Hello, my name is Beena Ammanath, and I am the executive director for Deloitte AI Institute. And today on AI Ignition, we have Dr. Andrew Ng. Andrew is a world-renowned leader in the space of AI. He is the founder and CEO of Landing AI, he is the founder of Deeplearning.AI, and he is the general partner for AI fund. Welcome, Andrew. Andrew Ng (Andrew): Thank you, Beena. Good to see you here. I think despite the circumstances in society and the global tragedy of the pandemic, I think many of us are privileged to have work of a nature that we could do it from home. Beena: Yes, absolutely, totally agree with you. Now, I am curious to hear, because you do so much, you do a lot, you lead a number of companies. Can you share a day in the life of Dr. Andrew Ng? Andrew: I spend a lot of time working. I am privileged to genuinely love my work. The other day I woke up at 5 a.m. on a Monday morning just excited to start work. I’m probably not setting a good example, probably other people shouldn’t do that.
    [Show full text]
  • Using Neural Networks for Image Classification
    San Jose State University SJSU ScholarWorks Master's Projects Master's Theses and Graduate Research Spring 5-18-2015 Using Neural Networks for Image Classification Tim Kang San Jose State University Follow this and additional works at: https://scholarworks.sjsu.edu/etd_projects Part of the Artificial Intelligence and Robotics Commons Recommended Citation Kang, Tim, "Using Neural Networks for Image Classification" (2015). Master's Projects. 395. DOI: https://doi.org/10.31979/etd.ssss-za3p https://scholarworks.sjsu.edu/etd_projects/395 This Master's Project is brought to you for free and open access by the Master's Theses and Graduate Research at SJSU ScholarWorks. It has been accepted for inclusion in Master's Projects by an authorized administrator of SJSU ScholarWorks. For more information, please contact [email protected]. Using Neural Networks for Image Classification San Jose State University, CS298 Spring 2015 Author: Tim Kang ([email protected]), San Jose State University ​ ​ Advisor: Robert Chun ([email protected]), San Jose State University ​ ​ Committee: Thomas Austin ([email protected]), San Jose State University ​ ​ Committee: Thomas Howell ([email protected]), San Jose State University ​ ​ Abstract This paper will focus on applying neural network machine learning methods to images for the purpose of automatic detection and classification. The main advantage of using neural network methods in this project is its adeptness at fitting non­linear data and its ability to work as an unsupervised algorithm. The algorithms will be run on common, publically available datasets, namely the MNIST and CIFAR­10, so that our results will be easily reproducible.
    [Show full text]
  • Nature-Learning-Machines.Pdf
    NEWS FEATURE THE LEARNING MACHINES Using massive amounts of data to recognize photos and speech, deep-learning computers are taking a big step towards true artificial intelligence. BY NICOLA JONES hree years ago, researchers at the help computers to crack messy problems that secretive Google X lab in Mountain humans solve almost intuitively, from recog- View, California, extracted some nizing faces to understanding language. 10 million still images from YouTube Deep learning itself is a revival of an even videos and fed them into Google Brain older idea for computing: neural networks. — a network of 1,000 computers pro- These systems, loosely inspired by the densely grammed to soak up the world much as a interconnected neurons of the brain, mimic T BRUCE ROLFF/SHUTTERSTOCK human toddler does. After three days looking human learning by changing the strength of for recurring patterns, Google Brain decided, simulated neural connections on the basis of all on its own, that there were certain repeat- experience. Google Brain, with about 1 mil- ing categories it could identify: human faces, lion simulated neurons and 1 billion simu- human bodies and … cats1. lated connections, was ten times larger than Google Brain’s discovery that the Inter- any deep neural network before it. Project net is full of cat videos provoked a flurry of founder Andrew Ng, now director of the jokes from journalists. But it was also a land- Artificial Intelligence Laboratory at Stanford mark in the resurgence of deep learning: a University in California, has gone on to make three-decade-old technique in which mas- deep-learning systems ten times larger again.
    [Show full text]
  • Trigger Word Detection
    CS230: Lecture 2 Deep Learning Intuition Kian Katanforoosh Kian Katanforoosh, Andrew Ng, Younes Bensouda Mourri Recap Kian Katanforoosh, Andrew Ng, Younes Bensouda Mourri Learning Process Input Output Model = Architecture + 0 Parameters Things that can change Loss - Activation function - Optimizer Gradients - Hyperparameters - … Kian Katanforoosh, Andrew Ng, Younes Bensouda Mourri Logistic Regression as a Neural Network image2vector /255 x(i) ⎛ 255⎞ 1 ⎜ ⎟ /255 (i) 231 x2 ⎜ ⎟ 0.73 > 0.5 ⎜... ⎟ … … … wT x(i) + b σ 0.73 “it’s a cat” ⎜ ⎟ /255 (i) 94 xn−1 ⎜ ⎟ /255 (i) ⎝142⎠ xn Kian Katanforoosh, Andrew Ng, Younes Bensouda Mourri Multi-class image2vector /255 x(i) 0.12 < 0.5 255 1 T (i) ⎛ ⎞ w x + b σ 0.12 Dog? ⎜ ⎟ /255 (i) 231 x2 ⎜ ⎟ 0.73 > 0.5 ⎜... ⎟ … … wT x(i) + b σ 0.73 Cat? ⎜ ⎟ /255 (i) 94 xn−1 0.04 < 0.5 ⎜ ⎟ T (i) Giraffe? /255 w x + b σ 0.04 (i) ⎝142⎠ xn Kian Katanforoosh, Andrew Ng, Younes Bensouda Mourri Neural Network (Multi-class) image2vector /255 x(i) 255 1 T (i) ⎛ ⎞ w x + b σ ⎜ ⎟ /255 (i) 231 x2 ⎜ ⎟ ⎜... ⎟ … … wT x(i) + b σ ⎜ ⎟ /255 (i) 94 xn−1 ⎜ ⎟ T (i) /255 w x + b σ (i) ⎝142⎠ xn Kian Katanforoosh, Andrew Ng, Younes Bensouda Mourri Neural Network (1 hidden layer) image2vector Hidden layer /255 x(i) ⎛ 255⎞ 1 [1] a1 ⎜ ⎟ /255 (i) 231 x2 output layer ⎜ ⎟ [1] [2] … … a a 0.73 ⎜... ⎟ 2 1 0.73 > 0.5 ⎜ ⎟ /255 (i) 94 xn−1 ⎜ ⎟ [1] /255 Cat (i) a3 ⎝142⎠ xn Kian Katanforoosh, Andrew Ng, Younes Bensouda Mourri Deeper network: Encoding Hidden layer (i) [1] Hidden layer x1 a1 [2] a1 (i) [1] x2 a2 output layer (i) [2] [3] a2 a1 yˆ (i) [1] x3 a3 [2] a3 (i) [1] x4 a4 Technique called “encoding” Kian Katanforoosh, Andrew Ng, Younes Bensouda Mourri Let’s build intuition on concrete applications Kian Katanforoosh, Andrew Ng, Younes Bensouda Mourri Today’s outline We will learn tips and tricks to: - Analyze a problem from a I.
    [Show full text]