In-depth technical guides covering Classical Machine Learning metrics, Large Language Model evaluation, Transformer mechanics, parameter tuning, and production RAG architecture.
A deep dive into confusion matrices, Type I and Type II errors, accuracy paradox on imbalanced data, and harmonic mean F-beta formulations.
Classification metrics almost all derive from the confusion matrix. Learn how Accuracy, Precision, Recall, and F1-Score behave under real-world data distributions.
Explore threshold-agnostic ROC curves, probability ranking, Precision-Recall AUC for rare classes, and multi-class macro, micro, and weighted averaging techniques.
A practical guide to continuous error metrics (MAE, MSE, RMSE, R²) and computer vision spatial metrics (Intersection over Union, Dice Coefficient, mAP).
How do we automatically measure LLM response quality? Explore probability perplexity, exact string overlap (BLEU/ROUGE), and BERTScore semantic alignment.
Discover how top AI labs replace human raters with LLM-as-a-Judge frameworks like G-Eval, evaluating correctness, relevance, and hallucination rates.
A practical operational guide covering RAG metrics (Context Precision, Recall, Groundedness) and physical serving metrics (TTFT, VRAM sizing, Throughput).
Explore linear hyperplanes, sigmoid logistic log-loss, L1 Lasso feature selection, L2 Ridge shrinkage, KNN distance metrics, and Naive Bayes independence assumptions.
Master decision tree split criteria (Gini vs Entropy), Random Forest bagging, and gradient boosted powerhouses like XGBoost, LightGBM, and CatBoost.
Explore SVM maximum margin boundaries, unsupervised clustering (K-Means, DBSCAN), dimensionality reduction (PCA, t-SNE, UMAP), and time series models.
Uncover how LLMs are engineered: Byte-Pair Encoding, Rotary Position Embeddings (RoPE), Self-Attention QKV matrices, RMSNorm, and SwiGLU activations.
From pretraining FLOP scaling laws ($6ND$) to Supervised Fine-Tuning (SFT), DPO alignment, and parameter-efficient fine-tuning with LoRA & 4-bit QLoRA.
Master decoding parameters: Temperature logits scaling, Top-P nucleus sampling, Top-K truncation, Repetition Penalties, and system prompt engineering.
A deep dive into document ingestion: PDF layout structure parsing, fixed-size vs semantic chunking, and small-to-big parent document retrieval.
Understand dense vector embeddings, contrastive training, ANN search indexing (HNSW graphs, IVF Voronoi cells), and Product Quantization (PQ).
Production-grade RAG: combine sparse BM25 with dense vectors via RRF, re-rank with Cross-Encoders, leverage HyDE, and evaluate using the RAG Triad.