ECE 580G Deep Learning for ECE
(Slides and code coming soon)
| Lecture |
Slides |
Video |
Code |
| Introduction to Object Oriented Programing |
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| Introduction to learning theory |
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| Introduction to Numerical Optimization (1) |
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| Introduction to Numerical Optimization (2) |
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| Introduction to TensorFLow 2.x (1) |
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| Introduction to TensorFLow 2.x (2) |
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| A visual understanding of MLP |
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| Convolutional Neural Networks |
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| Convolutions from the brain to the GPU |
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| Advanced Convolutional Neural Networks |
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| The inverted residual block - Layers subclassing |
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| tf.data - Image Augmentations |
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| Transfer Learning |
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| A discussion with Eugene Khvedchenya (Albumentations) |
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| Image Segmentation |
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| Grad Cam |
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| Introduction to Generative Adversarial Networks |
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ECE 566 Detection Theory
(Spring 2020) I served as co-instructor in Prof. Jessica Fridrich’s ECE 566 Detection Theory. Helped with making slides and/or given a few lectures on the following subjects:
| Lecture |
Slides |
| Introduction to Mixture Models |
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| Maximum Likelihood Estimation |
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| \chi square distribution |
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| Bayesian Hypothesis Testing (binary) |
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| Bayesian Hypothesis Testing (multiple hypotheses) |
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| Bayesian Hypothesis Testing (2 deterministic signals) |
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| Bayesian Hypothesis Testing (multiple deterministic signals) |
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| Introduction to Composite Hypothesis Testing |
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| Introduction to Composite Hypothesis Testing |
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| Composite Hypothesis Testing (bayesian approach) |
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| Composite Hypothesis Testing (GLRT) |
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| Asymptotic distribution of the GLRT |
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