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The exam specifies closed-book rules with one A4 sheet of notes permitted and requires students to explain work for partial credit. It includes multiple-choice questions and short-answer tasks, with topics spanning GANs, activation functions and vanishing gradients, non-saturating GAN objectives, batch normalization, Xavier initialization, multi-task learning, end-to-end learning, convolutional layer parameter counting, error analysis, and methods for generating adversarial examples.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":20,"@type":76,"position":81},"https://docshare.wps.com/document/exam/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/cs230-deep-learning-winter-quarter-2019-midterm-examination/459768/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/cs230-deep-learning-winter-quarter-2019-midterm-examination/459768.png","ImageObject",300,407,{"name":92,"@type":93},"Mimi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-21","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What are the exam rules regarding materials during CS230 Deep Learning midterm?","Question",{"text":112,"@type":113},"The exam is closed book and no laptops, notes, or textbooks are allowed. One A4 sheet (front and back) of notes may be used as a reference.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How is scoring affected for multiple-choice questions when answers are incorrect?",{"text":117,"@type":113},"No explanation is required for multiple-choice questions. There is no penalty for a wrong answer, but partial credit can be given for good explanations on written work.",{"name":119,"@type":110,"acceptedAnswer":120},"Which topics are covered in the multiple-choice section of this midterm?",{"text":121,"@type":113},"Topics include GANs for apple image generation, vanishing gradients from activation functions, squared-error regression gradient equations, non-saturating generator cost for GANs, batch normalization, Xavier initialization, multi-task learning, end-to-end learning advantages, convolutional layer parameter counting, error analysis, and adversarial example generation methods.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},459768,1791452115,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":19,"category_name":20,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":19,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},2336477974920,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","CS230: Deep Learning  \nWinter Quarter 2019 Stanford University  \nMidterm Examination  \n180 minutes  \n\n|  | Problem | Full Points | Your Score |\n| --- | --- | --- | --- |\n\n\n| 1 | Multiple Choice Questions | 13 |  |\n| --- | --- | --- | --- |\n| 2 | Short Answer Questions | 23 |  |\n| 3 | Convolutional Neural Networks | 28 |  |\n| 4 | Adversarial Attacks | 10 |  |\n| 5 | Loss comparisons | 23 |  |\n| 6 | The Optimizer | 20 |  |\n\n\n| Total | 117 |  |\n| --- | --- | --- |\n\nThe exam contains 29 pages including this cover page.  \n• This exam is closed book [i.e. no](i.e. no) laptops, notes, textbooks, etc. during the exam. However, you may use one A4 sheet (front and back) of notes as reference.  \n• In all cases, and especially if you're stuck or unsure of your answers, explain your work, including showing your calculations and derivations! We'll give partial credit for good explanations of what you were trying to do.  \nName:    \n[SUNETID: ](SUNETID:  @stanford.edu)[ @stanford.edu](SUNETID:  @stanford.edu)  \n[The Stanford University Honor Code:](The Stanford University Honor Code:)  \nI attest that I have not given or received aid in this examination, and that I have done my share and taken an active part in seeing to it that others as well as myself uphold the spirit and letter of the Honor Code.  \nSignature:    \nCS230  \nQuestion 1 (Multiple Choice Questions, 13 points)  \nFor each of the following questions, circle the letter of your choice. There is only ONE correct choice unless explicitly mentioned. No explanation is required. There is no penalty for a wrong answer.  \n(a) (1 point) Consider a Generative Adversarial Network (GAN) which successfully produces images of apples. Which of the following propositions is false?  \n(i) The generator aims to learn the distribution of apple images.  \n(ii) The discriminator can be used to classify images as apple vs. non-apple.  \n(iii) After training the GAN, the discriminator loss eventually reaches a constant value.  \n(iv) The generator can produce unseen images of apples.  \nSolution: (ii)  \n(b) (1 point) Which of the following activation functions can lead to vanishing gradients?  \n(i) ReLU  \n(ii) Tanh  \n(iii) Leaky ReLU  \n(iv) None of the above  \nSolution: (ii)  \n(c) (1 point) Consider a univariate regression y^ = wx where w 2 R, and x 2 R 1 􀀂 m. The cost function is the squared-error cost J = ~~1~~m k y^ 􀀀 y k2 . Which of the following equations is true?  \n(i) ~~@~~@Jw = ~~1~~m (y^ 􀀀 y)xT  \n(ii) ~~@~~@Jw = ~~1~~m (y^ 􀀀 y)x  \n(iii) ~~@~~@Jw = ~~2~~m (y^ 􀀀 y)xT  \n(iv) ~~@~~@Jw = ~~2~~m (y^ 􀀀 y)x  \nSolution: (iii)  \nCS230  \n(d) (1 point) Which of the following costs is the non-saturating generator cost for GANs (G is the generator and D is the discriminator)?  \n(i) J (G) = ~~1~~m P log 􀀀1 􀀀 D(G(z(i))) 􀀁  \n(ii) J (G) = 􀀀 ~~1~~m P log 􀀀 D (G(z(i))) 􀀁  \n(iii) J (G) = ~~1~~m P log 􀀀1 􀀀 G(D(z(i))) 􀀁  \n(iv) J (G) = 􀀀 ~~1~~m P log 􀀀 G (D(z(i))) 􀀁  \nSolution: (ii)  \n(e) (1 point) After training a neural network, you observe a large gap between the training accuracy (100%) and the test accuracy (42%) . Which of the following methods is commonly used to reduce this gap?  \n(i) Generative Adversarial Networks  \n(ii) Dropout  \n(iii) Sigmoid activation  \n(iv) RMSprop optimizer  \nSolution: (ii)  \n(f) (1 point) Which of the following is true about Batchnorm?  \n(i) Batchnorm is another way of performing dropout.  \n(ii) Batchnorm makes training faster.  \n(iii) In Batchnorm, the mean is computed over the features.  \n(iv) Batchnorm is a non-linear transformation to center the dataset around the origin  \nSolution: (ii)  \n(g) (1 point) Which of the following statements is true about Xavier Initialization?  \n(i) It is only used in fully connected neural networks.  \n(ii) It applies a scaling factor to the mean of the random weights.  \n(iii) It is commonly used in logistic regression.  \n(iv) The assumptions made are only valid at the beginning of training.  \nCS230  \nSolution: (iv)  \n(h) (1 point) When should multi-task lear","cbCaicpAhAuqzrxr","https://ap.wps.com/l/cbCaicpAhAuqzrxr","pdf",485780,29,"English","# Question 1 (Multiple Choice Questions)\n## (a) to (l)\n# Question 2 (Short Answer Questions)","[{\"question\":\"What are the exam rules regarding materials during CS230 Deep Learning midterm?\",\"answer\":\"The exam is closed book and no laptops, notes, or textbooks are allowed. One A4 sheet (front and back) of notes may be used as a reference.\"},{\"question\":\"How is scoring affected for multiple-choice questions when answers are incorrect?\",\"answer\":\"No explanation is required for multiple-choice questions. There is no penalty for a wrong answer, but partial credit can be given for good explanations on written work.\"},{\"question\":\"Which topics are covered in the multiple-choice section of this midterm?\",\"answer\":\"Topics include GANs for apple image generation, vanishing gradients from activation functions, squared-error regression gradient equations, non-saturating generator cost for GANs, batch normalization, Xavier initialization, multi-task learning, end-to-end learning advantages, convolutional layer parameter counting, error analysis, and adversarial example generation methods.\"}]","CS230 Deep Learning - Winter Quarter 2019 Midterm Examination | PDF",1790756830,73]