Posts Tagged "machine-learning"

Practical framework to determine when AI agents make sense for your use case. Learn when to build agents and when simpler approaches like prompt engineering or RAG work better.

Understand how LLMs choose words during generation. Learn temperature, top-k, and top-p sampling strategies to balance coherence, diversity, and task-appropriateness in generated text.

Critical review of LLM limitations in low-probability situations. Explores why AI practitioners should understand autoregressive training pressures before deploying LLMs for tasks requiring precise reasoning or uncommon patterns.

Learn to build a multi-label text classifier using DistilBERT with imbalanced classes. Covers binary cross-entropy loss, multi-hot encoding, and practical implementation strategies for handling multiple labels.

Understand t-SNE dimensionality reduction for visualizing high-dimensional data. Covers perplexity parameter tuning, implementation with TF-IDF vectors, and interactive visualization best practices.

Comprehensive introduction to generative LLMs covering basics, training processes, and real-world applications. Slides from talk delivered to 70+ attendees.