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CS22037 U5 4 Real Time Applications

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CS22037 U5 3 Formalization of Explanation Techniques and Evaluations, Adoption of Interpretable

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UNIT 5 CS22032 DNN P2-ENCODER DECODER RNN,LSTM,GRU

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UNIT 5 CS22032 DNN _SEQUENCE MODELS,RNN

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CS22037 U5 2 Challeneges and Taxnomy

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CS22037 U5 1 Time Series Forecasting, Natural Language Processing, and Computer Vision XAI

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CS22037 U4 6 Case Study

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CS22037 U4 5 Deep Visualization Techniques detailed with Real time

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CS22037 U4 4 Case Study Attention + Modularity +Feature Identification

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UNIT 4 CS22032 DNN P3_BATCH NORMALIZATION

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CS22037-U4 2 Learn to Explain – Feature Visualization Deep Visualization

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CS22037-U4-3-gradcam and Activation maps Sensitivity analysis

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CS22037-U4-1-Attention Mechanisms – Modular Networks – Feature Identification

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CS22037-U3-5 Case Study Real world Scenario (Explainable AI)

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CS22037-U3 -4 Real World Mapping

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CS22037-U3- 3-Optimizing sparse logical models such as decision trees, supervised disentanglement

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UNIT 4 CS22032 DNN P2

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UNIT 4 CS22032 DNN P1

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UNIT 3 CS22032 P3

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UNIT 3 CS22032 P2

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U3 2 Post Hoc Interpretability and Explanations

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U3 1 Interpretable & Explainable Algorithms

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UNIT 2 MODEL SELECTION,REGULARIZATION,WHITE DECAY,DROPOUT

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CS22037- U2 PHE Models, method & Comparison

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CS22037- U2 Realtime Concept Part 2

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CS22037-U2 Real Time Concept of AHE, PHE, IML & Beta Part 1

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CS22037- U2 IML, BETA MOdels

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CS22037-U2 Hybrid Models Model Validation Hyperparameters Model Selction & Visualization

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U2- Ante hoc,Concepts & Examples

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CS22032_U3_CNN& Constraints in CNN, Convolution in MLP

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