CCS355: Neural Networks and Deep Learning syllabus for CSE 2021 regulation (Professional Elective-VI)

Neural Networks and Deep Learning detailed syllabus for Computer Science & Engineering (CSE) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the CSE 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.

For Computer Science & Engineering 6th Sem scheme and its subjects, do visit CSE 6th Sem 2021 regulation scheme. For Professional Elective-VI scheme and its subjects refer to CSE Professional Elective-VI syllabus scheme. The detailed syllabus of neural networks and deep learning is as follows.

Course Objectives:

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Unit I

INTRODUCTION 6 Neural 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.

Unit II

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Unit III

THIRD-GENERATION NEURAL NETWORKS 6 Spiking Neural Networks-Convolutional Neural Networks-Deep Learning Neural Networks-Extreme Learning Machine Model-Convolutional Neural Networks: The Convolution Operation – Motivation -Pooling – Variants of the basic Convolution Function – Structured Outputs – Data Types – Efficient Convolution Algorithms – Neuroscientific Basis – Applications: Computer Vision, Image Generation, Image Compression.

Unit IV

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Unit V

RECURRENT NEURAL NETWORKS 6 Recurrent 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.

Lab Experiments:

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Additional Experiments:

1. Train a Deep learning model to classify a given image using pre trained model
2. Recommendation system from sales data using Deep Learning
3. Implement Object Detection using CNN
4. Implement any simple Reinforcement Algorithm for an NLP problem

Course Outcomes:

At the end of this course, the students will be able to:

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

Text Books:

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Reference Books:

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

For detailed syllabus of all the other subjects of Computer Science & Engineering 6th Sem, visit CSE 6th Sem subject syllabuses for 2021 regulation.

For all Computer Science & Engineering results, visit Anna University CSE all semester results direct link.