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Master LLM-as-a-Judge evaluation frameworks from scratch using Weave, tackling real-world challenges and practical considerations for generative AI applications.
Train a compact Zero-DCE model on unlabelled dark images for fast low-light enhancement.
Implement probability concepts such as surprise, entropy, cross-entropy, divergence, and Gaussian likelihoods to understand machine learning loss functions.
Work through calculus exercises for machine learning using little-o notation, symbolic Python, linear approximations, gradients, and gradient descent.
Exercises that treat matrices as composable functions, using shape checks, multiplication, batching, and concatenation to build machine learning intuition.
Practice linear algebra through notebook exercises on array shapes, dimension counting, transposes, and matrices as functions.
A deep dive into a fourth-place CommonLit Kaggle solution, from NLP data exploration and cross-validation to transformer modeling and Weights & Biases workflows.
See how probability, surprise, entropy, and Gaussian ideas explain what machine learning optimizes and why negative log probabilities become loss functions.
An intuition-first introduction to linear algebra for machine learning, using arrays, matrix multiplication, and singular value decomposition as programming and refactoring concepts.
Explore computer vision advancements with Weights & Biases and SmartCow. Learn about Ultralytics, object detection metrics, and practical implementations for enhancing ML projects.
Instrumenting W&B for PII detection: Learn to track experiments, analyze data, and perform error analysis in ML pipelines. Apply knowledge to a Kaggle competition using W&B Tables and Artifacts.
Master end-to-end LLM fine-tuning with W&B Models, from experiment tracking to model deployment in production. Learn to publish models, automate evaluations, and implement CI/CD workflows for efficient AI development.
Explore advanced RAG techniques for complex queries, diverse data sources, and evaluation methods to enhance system flexibility and performance in handling sophisticated user requests.
Explore advanced LLM-powered competitive programming using RAG for code generation. Learn to design robust architectures, implement AST-based similarity search, and enhance few-shot learning for AI agents.
Explore DSPy's innovative approach to code generation, enhancing AI capabilities for developers and researchers in neural information processing systems.
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