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
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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
Day97 Deep Learning Lecture Review - Lecture 15 (2)
Bias Mitigation Strategies: Loss Reweighting, Sampling & Synthetic Samples and Architectural Changes (OccamNets, Adversarial Training & DANN)
Day96 Deep Learning Lecture Review - Lecture 15 (1)
Model Comparison and Bias Mitigation; McNemar’s Test, Dataset Bias, and Bias Detection
Day95 Deep Learning Lecture Review - Lecture 14
AI Ethics; AI Safety, Key Issues, AGI (Artificial General Intelligence), and Current AI Models’ Challenges
Day94 Deep Learning Lecture Review - Lecture 13
Llama 3: Framework, Workflow (RMSNorm, Grouped Query Attention, RoPE, SwiGLU Attention), Pre-training & Post-training
Day93 Deep Learning Lecture Review - Fine-Tuning Models (2) & Prompt Engineering
Comparing Pre-trained model embeddings (ResNet+SBERT vs. CLIP) and Prompt Engineering (Short and Direct, Few-Shot Learning, & Expert Prompting)