Fundamentals of Deep Learning

UNIT I 

Introduction to Deep Learning: Introduction to Deep Learning, Bayesian Learning, Overview of Shallow Machine Learning, Difference between Deep Learning and Shallow Learning, Linear Classifiers ,Loss Function and Optimization Techniques -Gradient Descent and batch optimization.

UNIT II 

Introduction to Neural Network: Introduction to Neural Network, Biological Neuron, Idea of computational units, McCulloch–Pitts unit and Thresholding logic Artificial Neural Networks: Single Layer Neural Network, Multilayer Perceptron, Back Propagation through time. Architectural Design Issues.

UNIT III 

Training Deep Neural Networks: Difficulty of training deep neural networks, Activation Function, Evaluating, Improving and Tuning the ANN. Hyper parameters Vs Parameters, Greedy layer wise training, Recurrent Neural Networks, Long Short-Term Memory, Gated Recurrent Units, Bidirectional LSTMs, Bidirectional RNNs.

UNIT IV 

Convolutional Neural Networks: Convolutional Neural Networks, Building blocks of CNN, Transfer Learning , Pooling Layers , Convolutional Neural Network Architectures.Well known case studies: LeNet, AlexNet, VGG-16, ResNet, Inception Net.Applications in Vision, Speech, and Audio-Video.

Text Books:

1. Richard O. Duda,” Pattern classification, Wiley, 2022
2. Adam Gibson and Josh Patterson, “Deep Learning: A Practical approach”, 2017
3. Deep Learning, Ian Goodfellow and Yoshua Bengio and Aaron Courville, MIT Press, 2016.

Reference Books :

1. Charu C. Aggarwal, “Neural Networks and Deep Learning”, 2018
2. Duda, R.O. and Hart, P.E., Pattern classification. John Wiley & Sons, 2006.