Cyber Security

CCS355: Neural Networks and Deep Learning syllabus for Cyber Security 2021 regulation (Professional Elective-IV)

Neural Networks and Deep Learning detailed syllabus for Cyber Security (Cyber Security) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the Cyber Security 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 Cyber Security 6th Sem scheme and its subjects, do visit Cyber Security 6th Sem 2021 regulation scheme. For Professional Elective-IV scheme and its subjects refer to Cyber Security Professional Elective-IV syllabus scheme. The detailed syllabus of neural networks and deep learning is as follows.

Course Objectives:

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Unit I

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

Training 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.

Unit III

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Unit IV

History 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.

Unit V

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

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

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:

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Text Books:

  1. Ian Goodfellow, Yoshua Bengio, Aaron Courville, “Deep Learning”, MIT Press, 2016.
  2. Francois Chollet, “Deep Learning with Python”, Second Edition, Manning Publications, 2021.

Reference Books:

  1. Aurelien Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Oreilly, 2018.
  2. Josh Patterson, Adam Gibson, “Deep Learning: A Practitioner’s Approach”, O’Reilly 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 Cyber Security 6th Sem, visit Cyber Security 6th Sem subject syllabuses for 2021 regulation.

For all Cyber Security results, visit Anna University Cyber Security all semester results direct link.

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