{"id":55239,"date":"2023-08-28T07:11:54","date_gmt":"2023-08-28T07:11:54","guid":{"rendered":"https:\/\/www.inspirenignite.com\/anna-university\/al3502-deep-learning-for-vision-syllabus-for-aiml-2021-regulation\/"},"modified":"2023-08-28T07:11:54","modified_gmt":"2023-08-28T07:11:54","slug":"al3502-deep-learning-for-vision-syllabus-for-aiml-2021-regulation","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/anna-university\/al3502-deep-learning-for-vision-syllabus-for-aiml-2021-regulation\/","title":{"rendered":"AL3502: Deep Learning for Vision syllabus for AI&ML 2021 regulation"},"content":{"rendered":"

Deep Learning for Vision detailed syllabus for Artificial Intelligence & Machine Learning (AI&ML) for 2021 regulation curriculum has been taken from the Anna University<\/a> official website and presented for the AI&ML students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below. <\/p>\n

For Artificial Intelligence & Machine Learning 5th Sem scheme and its subjects, do visit AI&ML 5th Sem 2021 regulation scheme<\/a>. The detailed syllabus of deep learning for vision is as follows. <\/p>\n

Course Objectives:<\/h4>\n

Download the iStudy App for all syllabus and other updates.<\/a>
\"Get<\/a><\/h4>\n

Unit I<\/h4>\n

COMPUTER VISION BASICS
\nIntroduction to Image Formation, Capture and Representation; Linear Filtering, Correlation, Convolution
\nVisual Features and Representations: Edge, Blobs, Corner Detection; Visual Features extraction: Bag-of-words, VLAD; RANSAC, Hough transform.\n<\/p>\n

Unit II<\/h4>\n

INTRODUCTION TO DEEP LEARNING
\nDeep Feed-Forward Neural Networks – Gradient Descent – Back-Propagation and Other Differentiation Algorithms – Vanishing Gradient Problem – Mitigation – Rectified Linear Unit (ReLU) – Heuristics for Avoiding Bad Local Minima – Heuristics for Faster Training – Nestors Accelerated Gradient Descent – Regularization for Deep Learning – Dropout – Adversarial Training – Optimization for Training Deep Models.\n<\/p>\n

Unit III<\/h4>\n

Download the iStudy App for all syllabus and other updates.<\/a>
\"Get<\/a><\/h4>\n

Unit IV<\/h4>\n

CNN and RNN FOR IMAGE AND VIDEO PROCESSING
\nCNNs for Recognition, Verification, Detection, Segmentation: CNNs for Recognition and Verification (Siamese Networks, Triplet Loss, Contrastive Loss, Ranking Loss); CNNs for Detection: Background of Object Detection, R-CNN, Fast R-CNN. CNNs for Segmentation: FCN, SegNet.
\nRecurrent Neural Networks (RNNs): Review of RNNs; CNN + RNN Models for Video Understanding: Spatio-temporal Models, Action\/Activity Recognition\n<\/p>\n

Unit V<\/h4>\n

DEEP GENERATIVE MODELS
\nDeep Generative Models: Review of (Popular) Deep Generative Models: GANs, VAEs
\nVariants and Applications of Generative Models in Vision: Applications: Image Editing, Inpainting, Superresolution, 3D Object Generation, Security;
\nRecent Trends: Self-supervised Learning; Reinforcement Learning in Vision;\n<\/p>\n

Practical Exercises<\/h4>\n
    \n
  1. Implementation of basic Image processing operations including Feature Representation and Feature Extraction 2. Implementation of simple neural network<\/li>\n
  2. Study of pretrained deep neural network model for Images<\/li>\n
  3. CNN for Image classification<\/li>\n
  4. CNN for Image segmentation<\/li>\n
  5. RNN for video processing<\/li>\n
  6. Implementation of Deep Generative model for Image editing<\/li>\n<\/ol>\n

    Course Outcomes:<\/h4>\n

    Download the iStudy App for all syllabus and other updates.<\/a>
    \"Get<\/a><\/h4>\n

    Text Books:<\/h4>\n
      \n
    1. Ian Goodfellow Yoshua Bengio Aaron Courville, \u201cDeep Learning\u201d, MIT Press, 2017<\/li>\n
    2. Ragav Venkatesan, Baoxin Li, \u201cConvolutional Neural Networks in Visual Computing\u201d, CRC Press, 2018.<\/li>\n<\/ol>\n

      Reference Books:<\/h4>\n
        \n
      1. Rajalingappaa Shanmugamani ,Deep Learning for Computer Vision, Packt Publishing, 2018<\/li>\n
      2. David Forsyth, Jean Ponce, Computer Vision: A Modern Approach, 2002.<\/li>\n
      3. Modern Computer Vision with PyTorch, V.Kishore Ayyadevara, Yeshwanth Reddy, 2020 Packt Publishing Ltd<\/li>\n
      4. Goodfellow, Y, Bengio, A. Courville, \u201cDeep Learning\u201d, MIT Press, 2016.<\/li>\n
      5. Richard Szeliski, Computer Vision: Algorithms and Applications, 2010.<\/li>\n
      6. Simon Prince, Computer Vision: Models, Learning, and Inference, 2012.<\/li>\n
      7. https:\/\/nptel.ac.in\/<\/li>\n<\/li>\n<\/ol>\n

        For detailed syllabus of all other subjects of Artificial Intelligence & Machine Learning, 2021 regulation curriculum do visit AI&ML 5th Sem subject syllabuses for 2021 regulation<\/a>. <\/p>\n

        For all Artificial Intelligence & Machine Learning results, visit Anna University AI&ML all semester results<\/a> direct link. <\/p>\n","protected":false},"excerpt":{"rendered":"

        Deep Learning for Vision detailed syllabus for Artificial Intelligence & Machine Learning (AI&ML) for 2021 regulation curriculum has been taken from the Anna University official website and presented for the […]<\/p>\n","protected":false},"author":2297,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[48,157],"tags":[],"class_list":["post-55239","post","type-post","status-publish","format-standard","hentry","category-5th-sem","category-aiml"],"_links":{"self":[{"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/posts\/55239","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/users\/2297"}],"replies":[{"embeddable":true,"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/comments?post=55239"}],"version-history":[{"count":0,"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/posts\/55239\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/media?parent=55239"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/categories?post=55239"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/tags?post=55239"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}