NEURAL NETWORKS AND DEEP LEARNING CST 395 CS 5TH SEMESTER HONORS COURSE NOTES - Dr Binu V P, 9847390760
Previous Year Question Papers CST 395 Neural Network and Deep Learning
Module 1 ( Basics of Machine Learning)
Overview of Machine LearningMachine Learning Algorithm
Linear Regression
Capacity, Overfitting and Underfitting
Regularization
Hyperparameters and Validation Sets
Estimators, Bias , Variance and Consistency
Challenges In Machine Learning
Linear and Logistic Regression ( Python code)
Differentiation of sigmoid and cross entropy function
Gradient Descent, Stochastic, Batch and mini batch gradient descent
Regression with Gradient Descent
Module-2 (Neural Networks)
Introduction to Neural Networks
Application of Neural Networks
Basic Architecture- Single Layer Neural Network
Power of Function Composition- Non Linear Activation XOR
Choice of activation and loss functions
Multi Layer Neural Network, Back Propagation
Implementation of a two layer network(XOR) with sigmoid activation function
Practical Issues in Neural Network Training
Techniques to prevent over fitting in Neural Network
Neural network and training ( additional reading)
Module-3 ( Deep Neural Network)
Vanishing and exploding gradient problems
Generalizing the Deep Neural Networks
Generalization issues in Model Tuning and Evaluation
Penalty Based regularization-L1 and L2
Module-4 ( Convolutional Neural Network -CNN)
Hierarchical Feature Engineering
Module-5 ( Recurrent Neural Network)
Language Modelling Example of RNN
Bidirectional Recurrent Networks
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