Fine-Tuning Vision Transformer Classifier for EyePacs Dataset Quality Model - Part 1
Fine-tune a Vision Transformer for image quality assessment using the EyeQ dataset, a subset of EyePacs, to enhance diabetic retinopathy detection capabilities.
Fine-Tuning Vision Transformer for Diabetic Retinopathy Detection - Part 2
Fine-tune a Vision Transformer for diabetic retinopathy detection using EyeQ dataset. Learn to adapt pretrained models for specialized medical image classification tasks.
Unlocking Mixture of Experts - From One Know-it-all to a Group of Jedi Masters
Explore Mixture of Experts technique to enhance predictive powers. Dive into principles, challenges, and applications. Learn when to use MoE, discuss business metrics, and access resources for implementation.
Fine-Tuning ViT Classifier for EyePacs Dataset Quality Model - Tutorial en Español
Personaliza un Vision Transformer con tu propio dataset para analizar la calidad de imágenes oculares utilizando el conjunto de datos EyeQ, un subconjunto de EyePacs.
How to Finetune and Develop Your Own Large Language Model
Explore techniques for fine-tuning and developing custom large language models, enhancing your AI capabilities and understanding of advanced NLP concepts.
Tuning Foundation Models: A Comprehensive Guide
Explore techniques for customizing foundation models with your data, improving performance across tasks while understanding limitations and future prospects.
Your Model, Your Data, Your Business: A Privacy-First Approach to Generative AI
Quickly train and fine-tune production AI models using your data, owning your model in the cloud or on-premises with a privacy-first approach.
Reinforcement Learning with Human Feedback
Explore how human feedback enhances AI models, improving text generation and responsiveness. Learn RLHF techniques for fine-tuning large language models with Luis Serrano, PhD.
Want High Performing LLMs? It's All About Your Data - Importance of Quality Data in AI Development
Explore data-centric AI development for high-performing LLMs. Learn pre-training techniques, avoid common pitfalls, and understand the critical role of quality data in model performance.
Fine-Tuning LLMs: Best Practices and When to Go Small - Lecture 124
Explore best practices for fine-tuning LLMs, including strategies for improving performance, overcoming challenges, and determining when to use smaller models for specific tasks.
Understanding the LLM Economics: The $360k Question - Lecture
Explore LLM economics, comparing costs of RAG vs fine-tuning and open-source vs commercial models. Gain insights on optimizing expenses for production-ready LLM applications.
Evaluating LLM Applications - Insights from Shahul Es
Explore LLM application evaluation with Shahul Es. Learn about debugging, benchmarks, custom data distributions, and fine-tuning to enhance model performance in this insightful discussion.
LLM Evaluation: Challenges and Best Practices - MLOps Podcast #210
Explore LLM evaluation complexities, Phoenix library's role, and customized assessments in AI applications. Gain insights on fine-tuning, model selection, and production deployment for effective AI implementation.
From Research to Production: Fine-Tuning and Aligning LLMs
Learn essential steps for transitioning LLMs to production, focusing on fine-tuning and alignment strategies. Explore Supervised Fine-Tuning, RLHF, and DPO for effective LLM alignment with production goals.
Handling Multi-Terabyte LLM Checkpoints - MLOps Podcast #228
Explore techniques for handling multi-terabyte LLM checkpoints, including storage options, saving and loading strategies, and infrastructure considerations for large-scale AI model training.
Retrieval Augmented Generation - Techniques and Applications
Explore Retrieval Augmented Generation with Syed Asad. Dive into production issues, embedding models, inference layer experiments, and the evolving landscape of AI engineering in this insightful discussion.
Alignment is Real - Fine-Tuning vs. Prompting in Domain-Specific Tasks
Explore fine-tuning techniques for domain-specific LLMs, discussing strategies for feedback integration, RAG implementation, and AI infrastructure decisions to achieve significant performance improvements.
Fine-Tuning ViT Classifier with Custom Data and Docker Inference - Part 4
Fine-tune a ViT classifier with custom data and deploy using Docker and FastAPI for efficient image classification in production environments.
LLM Quantization: Why Size Matters
Explore LLM quantization techniques, their impact on model size, and practical applications in training, fine-tuning, and inference. Gain insights into scaling laws and optimization strategies.
Langchain Document Summarization Using Fine-Tuned T5 and Clustering
Aprenda a utilizar Langchain para resumir textos usando T5 ajustado e clustering, com demonstração prática e notebooks disponíveis.
Fine-Tuning Foundation Models with Azure Machine Learning Studio
Fine-tune a BERT foundation model using Azure Machine Learning Studio for enhanced natural language processing capabilities.