Objectives
- Introduce major deep learning algorithms, the problem settings, and
their applications to solve real world problems.
Learning Outcomes
- Identify the deep learning algorithms which are more appropriate for various types of
learning tasks in various domains.
- Implement deep learning algorithms and solve real-world problems.
Course content
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Introduction:
Various paradigms of earning problems, Perspectives and Issues in deep learning framework, review of fundamental learning techniques.
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Feedforward neural network:
Artificial Neural Network, activation function, multi-layer neural network.
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Training Neural Network:
Risk minimization, loss function, backpropagation, regularization, model selection, and optimization.
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Conditional Random Fields:
Linear chain, partition function, Markov network, Belief propagation, Training CRFs, Hidden Markov Model, Entropy.
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Deep Learning:
Deep Feed Forward network, regularizations, training deep models, dropouts, Convolutional Neural Network, Recurrent Neural Network, Deep Belief Network.
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Probabilistic Neural Network:
Hopfield Net, Boltzman machine, RBMs, Sigmoid net, Autoencoders.
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Deep Learning research:
Object recognition, sparse coding, computer vision, natural language processing.
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Deep Learning Tools:
Caffe, Theano, Torch.
Text Books
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T1. Goodfellow, I., Bengio,Y., and Courville, A., Deep Learning, MIT Press, 2016..
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T2. Bishop, C. ,M., Pattern Recognition and Machine Learning, Springer, 2006.
Reference Books
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R1. Yegnanarayana, B., Artificial Neural Networks PHI Learning Pvt. Ltd, 2009.
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R2. Golub, G.,H., and Van Loan,C.,F., Matrix Computations, JHU Press,2013.
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R3. Satish Kumar, Neural Networks: A Classroom Approach, Tata McGraw-Hill Education, 2004.
Books on Optimization Techniques
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A. Ravindran, K. M. Ragsdell , and G. V. Reklaitis , ENGINEERING OPTIMIZATION: Methods and Applications , John Wiley & Sons, Inc. , 2016..
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A. Antoniou, W. S. Lu, PRACTICAL OPTIMIZATION Algorithms and Engineering Applications, Springer , 2007.