{"id":56218,"date":"2023-08-28T16:23:56","date_gmt":"2023-08-28T16:23:56","guid":{"rendered":"https:\/\/www.inspirenignite.com\/anna-university\/ccs355-neural-networks-and-deep-learning-syllabus-for-aiml-2021-regulation-professional-elective-i\/"},"modified":"2023-08-28T16:23:56","modified_gmt":"2023-08-28T16:23:56","slug":"ccs355-neural-networks-and-deep-learning-syllabus-for-aiml-2021-regulation-professional-elective-i","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/anna-university\/ccs355-neural-networks-and-deep-learning-syllabus-for-aiml-2021-regulation-professional-elective-i\/","title":{"rendered":"CCS355: Neural Networks and Deep Learning syllabus for AI&ML 2021 regulation (Professional Elective-I)"},"content":{"rendered":"

Neural Networks and Deep Learning detailed syllabus for Artificial Intelligence & Machine Learning (AI&ML) for 2021 regulation curriculum has been taken from the Anna Universities<\/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>. For Professional Elective-I scheme and its subjects refer to AI&ML Professional Elective-I syllabus scheme<\/a>. The detailed syllabus of neural networks and deep learning 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

INTRODUCTION
\nNeural Networks-Application Scope of Neural Networks-Artificial Neural Network: An IntroductionEvolution of Neural Networks-Basic Models of Artificial Neural Network- Important Terminologies of ANNs-Supervised Learning Network.\n<\/p>\n

Unit II<\/h4>\n

(ASSOCIATIVE MEMORY AND UNSUPERVISED LEARNING NETWORKS
\nTraining Algorithms for Pattern Association-Autoassociative Memory Network-Heteroassociative Memory Network-Bidirectional Associative Memory (BAM)-Hopfield Networks-Iterative Autoassociative Memory Networks-Temporal Associative Memory Network-Fixed Weight Competitive Nets-Kohonen Self-Organizing Feature Maps-Learning Vector Quantization-Counter propagation Networks-Adaptive Resonance Theory Network.\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

DEEP FEEDFORWARD NETWORKS
\nHistory of Deep Learning- A Probabilistic Theory of Deep Learning- Gradient Learning – Chain Rule and Backpropagation – Regularization: Dataset Augmentation – Noise Robustness -Early Stopping, Bagging and Dropout – batch normalization- VC Dimension and Neural Nets.\n<\/p>\n

Unit V<\/h4>\n

RECURRENT NEURAL NETWORKS
\nRecurrent Neural Networks: Introduction – Recursive Neural Networks – Bidirectional RNNs – Deep Recurrent Networks – Applications: Image Generation, Image Compression, Natural Language Processing. Complete Auto encoder, Regularized Autoencoder, Stochastic Encoders and Decoders, Contractive Encoders.\n<\/p>\n

Lab Experiments<\/h4>\n
    \n
  1. Implement simple vector addition in TensorFlow.<\/li>\n
  2. Implement a regression model in Keras.<\/li>\n
  3. Implement a perceptron in TensorFlow\/Keras Environment.<\/li>\n
  4. Implement a Feed-Forward Network in TensorFlow\/Keras.<\/li>\n
  5. Implement an Image Classifier using CNN in TensorFlow\/Keras.<\/li>\n
  6. Improve the Deep learning model by fine tuning hyper parameters.<\/li>\n
  7. Implement a Transfer Learning concept in Image Classification.<\/li>\n
  8. Using a pre trained model on Keras for Transfer Learning<\/li>\n
  9. Perform Sentiment Analysis using RNN<\/li>\n
  10. Implement an LSTM based Autoencoder in TensorFlow\/Keras.<\/li>\n
  11. Image generation using GAN<\/li>\n<\/ol>\n

    Additional Experiments:<\/h4>\n

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

    Course Outcomes:<\/h4>\n

    At the end of this course, the students will be able to:<\/p>\n

      \n
    1. Apply Convolution Neural Network for image processing.<\/li>\n
    2. Understand the basics of associative memory and unsupervised learning networks.<\/li>\n
    3. Apply CNN and its variants for suitable applications.<\/li>\n
    4. Analyze the key computations underlying deep learning and use them to build and train deep neural networks for various tasks.<\/li>\n
    5. Apply autoencoders and generative models for suitable applications.<\/li>\n<\/ol>\n

      Text Books:<\/h4>\n
        \n
      1. Ian Goodfellow, Yoshua Bengio, Aaron Courville, \u201cDeep Learning\u201d, MIT Press, 2016.<\/li>\n
      2. Francois Chollet, \u201cDeep Learning with Python\u201d, Second Edition, Manning Publications, 2021.<\/li>\n<\/ol>\n

        Reference Books:<\/h4>\n
          \n
        1. Aurelien Geron, \u201cHands-On Machine Learning with Scikit-Learn and TensorFlow\u201d, Oreilly, 2018.<\/li>\n
        2. Josh Patterson, Adam Gibson, \u201cDeep Learning: A Practitioner\u2019s Approach\u201d, O\u2019Reilly Media, 2017.<\/li>\n
        3. Charu C. Aggarwal, \u201cNeural Networks and Deep Learning: A Textbook\u201d, Springer International Publishing, 1st Edition, 2018.<\/li>\n
        4. Learn Keras for Deep Neural Networks, Jojo Moolayil, Apress,2018<\/li>\n
        5. Deep Learning Projects Using TensorFlow 2, Vinita Silaparasetty, Apress, 2020<\/li>\n
        6. Deep Learning with Python, FRANQOIS CHOLLET, MANNING SHELTER ISLAND,2017.<\/li>\n
        7. S Rajasekaran, G A Vijayalakshmi Pai, \u201cNeural Networks, FuzzyLogic and Genetic Algorithm, Synthesis and Applications\u201d, PHI Learning, 2017.<\/li>\n
        8. Pro Deep Learning with TensorFlow, Santanu Pattanayak, Apress,2017<\/li>\n
        9. James A Freeman, David M S Kapura, \u201cNeural Networks Algorithms, Applications, and Programming Techniques\u201d, Addison Wesley, 2003.<\/li>\n<\/li>\n<\/ol>\n

          For detailed syllabus of all the other subjects of Artificial Intelligence & Machine Learning 5th Sem, 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":"

          Neural Networks and Deep Learning detailed syllabus for Artificial Intelligence & Machine Learning (AI&ML) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for […]<\/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":[157],"tags":[],"class_list":["post-56218","post","type-post","status-publish","format-standard","hentry","category-aiml"],"_links":{"self":[{"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/posts\/56218","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=56218"}],"version-history":[{"count":0,"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/posts\/56218\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/media?parent=56218"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/categories?post=56218"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/tags?post=56218"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}