Deep Learning detailed syllabus for Information Technology (IT) for 2019 regulation curriculum has been taken from the Anna Universities official website and presented for the IT 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 Information Technology 8th Sem scheme and its subjects, do visit IT 8th Sem 2019 regulation scheme. For Professional Elective-VII scheme and its subjects refer to IT Professional Elective-VII syllabus scheme. The detailed syllabus of deep learning is as follows.
Course Objective:
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Unit I
Basics of Neural Networks
Basic Concept of Neurons – Perceptron Algorithm – Feed Forward and Backpropagation Networks.
Suggested Activities:
- Discussion of role of neural networks.
- External learning – Boltzmann Machine, perceptron.
- Practical – Implementation of simple neural network in Matlab
Suggested Evaluation Methods:
- Tutorials on perceptron.
- Assignments on backpropagation networks.
- Quizzes on neural networks.
Unit II
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Unit III
Convolutional Neural Networks
CNN Architectures – Convolution – Pooling Layers – Transfer Learning – Image Classification using Transfer Learning – Recurrent and Recursive Nets – Recurrent Neural Networks – Deep Recurrent Networks – Recursive Neural Networks – Applications.
Suggested Activities:
- Discussion of role of convolutional networks in Machine Learning.
- External learning – Concept of convolution and need for Pooling.
Suggested Evaluation Methods:
- Tutorials on image classification and recurrent nets.
- Assignments on image classification performances.
- Quizzes on convolutional neural networks.
Unit IV
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Unit V
Applications of Deep Learning
Images segmentation – Object Detection – Automatic Image Captioning – Image generation with Generative adversarial networks – Video to Text with LSTM models – Attention models for Computer Vision – Case Study: Named Entity Recognition – Opinion Mining using Recurrent Neural Networks – Parsing and Sentiment Analysis using Recursive Neural Networks – Sentence Classification using Convolutional Neural Networks – Dialogue Generation with LSTMs.
Suggested Activities:
- Discussion of role of deep learning in image and NLP applications.
- External learning – NLP concepts.
- Practical – Implementation of simple deep learning for object detection and recognition in images.
Suggested Evaluation Methods:
- Tutorials on images segmentation.
- Assignments on parsing and sentiment analysis.
- Quizzes on deep learning applications
Course Outcome:
For the complete syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier..
Text Books:
- Ian J. Goodfellow, Yoshua Bengio, Aaron Courville, “Deep Learning”, MIT Press, 2017.
- Francois Chollet, “Deep Learning with Python”, Manning Publications, 2018
References:
- Phil Kim, “Matlab Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence”, Apress, 2017.
- Ragav Venkatesan, Baoxin Li, “Convolutional Neural Networks in Visual Computing”, CRC Press, 2018.
- Navin Kumar Manaswi, “Deep Learning with Applications Using Python”, Apress, 2018.
- Joshua F. Wiley, “R Deep Learning Essentials”, Packt Publications, 2016.
For detailed syllabus of all the other subjects of Information Technology 8th Sem, visit IT 8th Sem subject syllabuses for 2019 regulation.
For all Information Technology results, visit Anna University IT all semester results direct link.