JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

DEEP LEARNING LAB

M.Tech I Year II Semester (R22)

Course Objectives:

  • 1. To Build The Foundation Of Deep Learning.
  • 2. To Understand How To Build The Neural Network.
  • 3. To enable students to develop successful machine learning concepts.

Course Outcomes:

  • 1. Upon the Successful Completion of the Course, the Students would be able to:
  • 2. Learn The Fundamental Principles Of Deep Learning.
  • 3. Identify The Deep Learning Algorithms For Various Types of Learning Tasks in various domains.
  • 4. Implement Deep Learning Algorithms And Solve Real-world problems.

LIST OF EXPERIMENTS:

  • 1. Setting up the Spyder IDE Environment and Executing a Python Program
  • 2. Installing Keras, Tensorflow and Pytorch libraries and making use of them
  • 3. Applying the Convolution Neural Network on computer vision problems
  • 4. Image classification on MNIST dataset (CNN model with Fully connected layer)
  • 5. Applying the Deep Learning Models in the field of Natural Language Processing
  • 6. Train a sentiment analysis model on IMDB dataset, use RNN layers with LSTM/GRU notes
  • 7. Applying the Autoencoder algorithms for encoding the real-world data
  • 8. Applying Generative Adversial Networks for image generation and unsupervised tasks.

TEXT BOOKS:

  • 1. Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville, MIT Press.
  • 2. The Elements of Statistical Learning by T. Hastie, R. Tibshirani, and J. Friedman, Springer.
  • 3. Probabilistic Graphical Models. Koller, and N. Friedman, MIT Press.

REFERENCES:

  • 1. Bishop, C, M., Pattern Recognition and Machine Learning, Springer, 2006.
  • 2. Yegnanarayana, B., Artificial Neural Networks PHI Learning Pvt. Ltd, 2009.
  • 3. Golub, G.H., and Van Loan C.F., Matrix Computations, JHU Press, 2013.
  • 4. Satish Kumar, Neural Networks: A Classroom Approach, Tata McGraw-Hill Education, 2004. Extensive Reading:
  • http://www.deeplearning.net
  • https://www.deeplearningbook.org/
  • https://developers.google.com/machine-learning/crash-course/ml-intro
  • www.cs.toronto.edu/~fritz/absps/imagenet.pdf
  • http://neuralnetworksanddeeplearning.com/

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