LLMOps: Personalizar Video Classifier (ViViT) con tu Propia Data
Personaliza un Video Vision Transformer (ViViT) con tu propio dataset utilizando un modelo preentrenado de Google y entrena un clasificador personalizado.
LLMOps: Inferencia de Clasificador ViT Fine-Tuned en CPU con C#
Aprende a realizar inferencia en C# con un Vision Transformer personalizado en formato ONNX utilizando CPU para clasificación de imágenes.
LLMOps: Converting Fine-Tuned ViT Classifier to ONNX for CPU Inference
Convierte un Vision Transformer personalizado a ONNX y realiza inferencia en CPU, optimizando el rendimiento del modelo de clasificación de imágenes.
Fine-Tuning ViT Classifier for Retinal Image Detection - Part 2 (Español)
Personaliza un Vision Transformer para clasificar imágenes de retina, detectando ojos enfermos usando el dataset EyeQ. Aprende a ajustar modelos pre-entrenados para tareas específicas.
Fine-Tuning ViT Classifier for Retina Images and Converting to ONNX Format - Part 3
Personaliza un Vision Transformer con tu dataset de imágenes de retina y conviértelo a formato ONNX para optimizar el rendimiento y la portabilidad.
Fine-Tuning ViT Classifier for Retinal Images with AKS Inference - Part 4 (Español)
Personaliza un Vision Transformer con tu dataset de imágenes de retina y crea un punto de inferencia administrado en Azure Machine Learning Studio (AKS).
DIY VCO - Shaping Sawtooth to Square
Learn to build an analog VCO from scratch, adding stability, fine-tuning, and variable pulse width output. Covers thermistors, op-amps, waveshaping, and achieving modular-conform volume levels.
Next-Gen Detection: Harnessing LLMs for Sigma Rule Automation
Explore innovative approaches to automate Sigma rule generation using LLMs, comparing techniques like RAG, fine-tuning, and prompt-chaining for enhanced threat detection efficiency.
LLM Privacy Paradox: Balancing Data Utility with Security - BSidesSF 2024
Explore the balance between data utility and security in LLM fine-tuning, addressing concerns about potential sensitive data leaks and privacy risks in specialized AI models.
NevIR: Negation in Neural Information Retrieval
Explore negation's impact on neural information retrieval models, analyzing performance across architectures and proposing strategies to improve handling of negated queries.
How AI Impacts the Insurance Industry Today
Explore AI's impact on insurance, addressing data challenges and LLM applications. Learn how Snorkel's approach enhances datasets and satisfies regulators in enterprise AI implementation.
Leveraging Data to Unlock AI's True Potential in Enterprise Projects
Unlock AI's potential through data development. Learn to fine-tune LLMs for domain-specific tasks, leverage expert knowledge, and accelerate high-quality dataset creation using Snorkel Flow.
Is the Universe Finely-Tuned for Life? - Episode 2
Explore the fascinating question of cosmic fine-tuning with physicists Paul Davies and Fred Adams as they examine whether the universe's physical constants are precisely calibrated for life to exist.
OpenFold - Lessons and Insights From Rebuilding and Retraining AlphaFold2
Insights from rebuilding AlphaFold2: OpenFold's optimized implementation, training process analysis, and implications for protein structure prediction and broader applications in structural biology.
On Some Aspects of Cosmological Fine-Tuning
Explore fine-tuning in cosmology through Bayesian and maximum entropy frameworks. Examine probabilistic measurements, distribution families, and the learnability of fine-tuning concepts.
BSM and Naturalness - Lecture 5
Explore beyond Standard Model physics and naturalness concepts in particle physics, focusing on advanced theoretical frameworks and their implications for fundamental interactions.
BSM and Naturalness - Lecture 4
Explore beyond Standard Model physics and naturalness concepts in particle theory, focusing on advanced topics and recent developments in the field.
BSM and Naturalness - Lecture 3
Explore beyond Standard Model physics and naturalness concepts in particle theory, focusing on advanced topics and recent developments in the field.
Naturalness in Quantum Field Theory After the LHC - Part 1
Explore naturalness in Quantum Field Theory post-LHC experiments, delving into fundamental concepts and implications for particle physics research.
Naturalness in Quantum Field Theory After the LHC - Part 2
Explore advanced concepts in Quantum Field Theory, focusing on naturalness and its implications post-LHC experiments.
Naturalness in Quantum Field Theory after LHC - Part 3
Explore advanced concepts in quantum field theory, focusing on naturalness after LHC experiments. Delve into cutting-edge research and theoretical developments in particle physics.