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Build intelligent dialogue systems with LLMs, covering conversation management, slot filling, memory strategies, and chatbot evaluation techniques.
Apply model merging to combine task fine-tunes, blend styles, compose capabilities, and evaluate merged models using normalized scores and Pareto analysis.
HellaSwag evaluates physical commonsense reasoning with adversarial filtering designed to remove superficial cues and annotation artifacts.
Combine pre-trained models into one without retraining. Topics include weight averaging, task arithmetic, TIES sign-election merging, and DARE random pruning.
MMLU tests language models across 57 subjects from STEM to law. Topics include evaluation protocols, few-shot methods.
Explains how structured pruning removes entire attention heads, transformer layers, and feed-forward neurons to create smaller, faster models.
Explains how weight pruning reduces neural network size by removing redundant parameters.
Covers calibration in machine learning and how model confidence aligns with actual accuracy. Examines ECE, temperature scaling.
Explains how feature distillation, attention transfer, progressive distillation, and on-policy distillation extend knowledge distillation beyond soft labels.
Use Exact Match and token-level F1 to score NLP and question-answering systems, with normalization, partial credit, thresholds, and common scoring errors.
Build multi-layer guardrail systems for LLM applications, covering input validation, output safety, PII detection, topic enforcement.
Explains how differential privacy protects training data in language models, from the mathematical guarantee to DP-SGD, privacy budgets.
BERTScore evaluates text generation using contextual embeddings to measure semantic similarity.
Explains how knowledge distillation transfers a large teacher model's learned knowledge to a smaller student through soft label supervision.
Measure and communicate LLM confidence through calibration, verbalized uncertainty, semantic entropy, and trust-calibrated user interfaces.
Covers ROUGE metrics for summarization evaluation. Topics include ROUGE-N, ROUGE-L, and ROUGE-W, and how they differ from BLEU in assessing text quality.
Covers continual learning evaluation with forward transfer, backward transfer, forgetting metrics.
Examines architecture-based continual learning: progressive networks, PackNet, HAT masks, PathNet.
Explains how experience replay buffers, pseudo-rehearsal, and generative replay prevent catastrophic forgetting in continual learning systems.
Explains how transformer models power sentiment analysis, topic classification, and intent detection.
Explains how the BLEU score evaluates machine translation quality using modified n-gram precision and brevity penalties.
Explains how classification-based and rule-based content filters protect language model deployments.
Explains how Elastic Weight Consolidation and Synaptic Intelligence protect critical parameters to prevent catastrophic forgetting in continual learning.
Covers cross-entropy loss for language models. Topics include entropy, KL divergence, perplexity.
Design reliable LLM evaluations with representative test sets, suitable metrics, and controls for contamination, overfitting, and misleading averages.
Perplexity measures how well a language model predicts tokens. Covers cross-entropy, tokenization effects, vocabulary size, and fair model comparisons.
Examines neural text-to-speech systems from acoustic modeling to vocoding. Topics include Tacotron, FastSpeech.
Covers the continual learning problem, why neural networks catastrophically forget sequential tasks, and the three canonical learning scenarios.
Explains how modern AI systems integrate speech with large language models. Examines audio tokenization, neural codecs.
Whisper was trained on 680,000 hours of weakly supervised audio. Covers data filtering, multilingual objectives, scaling choices, and zero-shot transfer.
OpenAI Whisper's encoder-decoder architecture enables multilingual speech recognition. Explains how multitask training and special tokens unify transcription.
Explains how sparse autoencoders decompose language model activations into interpretable features using overcomplete dictionaries, sparsity constraints.
Explains how language models link generated claims to source documents, evaluate citation accuracy with NLI, and measure attribution precision and recall.
Explains how speech recognition systems transform raw audio waveforms into mel spectrograms using STFT, mel filterbanks.
Covers LLM cost management with token-based cost modeling, optimization techniques like caching and quantization, cost allocation.
Covers multimodal AI evaluation with VQA benchmarks, image captioning metrics (BLEU, CIDEr, CLIPScore), and text-to-image quality (FID).
Monitor LLM output quality in production: track BLEU, BERTScore, and semantic metrics, detect drift with KS tests and CUSUM, catch regressions with A/B testing.
Monitor production LLM systems with metrics collection, structured logging, dashboard design, alerting rules, and concept drift detection.
Explains how multimodal models use image-text pairs, interleaved documents, and visual instruction data.
Flamingo adds visual input to frozen language models with a Perceiver Resampler and gated cross-attention. Covers its architecture and training design.
Explains how the logit lens projects transformer hidden states into vocabulary space to reveal how predictions evolve.
LLaVA connects a frozen vision encoder to a language model through a projection layer. Covers visual instruction tuning, architecture, data, and evaluation.
Extract attention weights from transformer models, visualize head patterns, measure head entropy, and understand the key caveats.
Covers LLM caching strategies including prompt caching with KV reuse, semantic caching with embedding similarity, cache invalidation policies.
Route LLM requests intelligently across model tiers using rule-based, difficulty-based, and cascade strategies, plus A/B testing to validate routing decisions.
Auto-scale LLM serving infrastructure using GPU utilization, queue depth, and TTFT metrics. Topics include horizontal vs vertical scaling.
Explains how projection layers bridge vision encoders and LLMs. Topics include linear transformation, MLP projection.
Explains how vision encoders bridge pixels and language in multimodal AI systems. Examines SigLIP training objectives, high-resolution handling strategies.
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