Day107 Deep Learning Lecture Review - HW4 - Adjusting Probabilities into Real-World
HW4: Model Calibration (Platt Scaling & Label Smoothing) and Conformal Prediction (Naive and Adaptive Predictions Sets)
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Day106 Deep Learning Lecture Review- Enhancing DL Workflows (Data Distribution)
HW3: Optimization through Data Loading, Profiling, & Scaling, and Comparison of Data Parallel & Distributed Data Parallel
Day105 Deep Learning Lecture Review - Lecture 20 (End)
Model Drifting, Periodic Re-Training, Detecting Model Drift, Continual Learning (Pre-Trained Model, NCC), and Real-Time Machine Learning
Day104 Deep Learning Lecture Review - Lecture 19
Data-Centric AI: Label Noise, Selection Bias, Data Leakage, and Error Analysis for Model Improvement (Subgroup Errors)
Day103 Deep Learning Lecture Review - Lecture 18 (2)
Data-Centric AI: Active Learning, SEALS(Similarity Search for Efficient Active Learning), Dataset Pruning, and Data Engine
Day102 Deep Learning Lecture Review - Lecture 18 (1)
Data-Centric AI: Crowdsourcing, Methods to Estimate Annotator Quality, Neural Scaling Laws, Pareto Curves and Power Law
Day101 Deep Learning Lecture Review - Lecture 17 (2)
Variation of Conformal Prediction: Size of Calibration Set, Evaluation, and Group-Based & Adaptive Conformal Prediction
Day100 Deep Learning Lecture Review - Lecture 17 (1)
Understanding Conformal Prediction: Concepts, Applications, Marginal Coverage, and Recipes In Detail
Day99 Deep Learning Lecture Review - Lecture 16
Uncertainty in Deep Learning, Distribution Shifts, Model Calibration, and Out-of-Distribution (OOD) Detection
Day98 DL Review - Revisiting LLM models (Hand-Written Tree Map)
Language Models- Transfer Learning, Basic Concepts & Terminologies, Components of NLP Models, and Attention Mechanism