MLOPS: Inferencia de Modelo VIT ONNX en Azure ML Managed Endpoint - Español
Aprende a realizar inferencia en Azure Kubernetes Services utilizando un modelo Vision Transformer convertido a ONNX con Azure Machine Learning Studio y Python SDK v2.
MLOPS: Inferencia de Modelo ONNX en Azure ML Managed Endpoint AKS
Aprende a realizar inferencia en Azure Kubernetes Services utilizando un modelo ONNX y onnxruntime, creando un Managed Endpoint con Python SDK v2.
Converting BERT to ONNX and Inference with BERTTokenizer for C# - Machine Learning and Data Science
Convierte modelos Bert a ONNX y realiza inferencia con BERTTokenizer en C#. Aprende a implementar aplicaciones prácticas de machine learning.
Inference of Utilities and Time Preference in Sequential Decision-Making
Explore utility inference and time preference in sequential decision-making, focusing on mathematical finance applications and advanced analytical techniques.
Inference at Scale with Apache Beam
Learn to deploy and scale machine learning models efficiently using Apache Beam for distributed inference on CPUs and GPUs, with practical insights on parallelizing workloads.
Machine Learning with Apache Beam
Explore Apache Beam for distributed data pipelines in machine learning, covering inference, data processing, and training with hands-on demos for parallelizing ML workloads.
Model-Based Reasoning in Neurosymbolic Programming for Science
Explore model-based reasoning techniques and their applications in neurosymbolic programming for scientific problem-solving.
Machine Learning Deployment and Product Integration - Best Practices and Lessons Learned
Explore best practices for deploying ML models and integrating them into core business functions. Learn from real-world experiences in on-demand inference deployment.
Mean-Field Games Stochastic Differential Mean-Field Games by Kavita Ramanan
Explore stochastic differential mean-field games in this advanced lecture, delving into mathematical models for large-scale strategic interactions and their applications in various complex systems.
Modified Logarithmic Sobolev Inequalities - Theory
Explore modified logarithmic Sobolev inequalities in this theoretical lecture by Prasad Tetali, part of a program on advances in applied probability covering high-dimensional computation and limit theorems.
Test Time Compute: Sampling and Chain of Thought Techniques
Explore advanced inference techniques like sampling and chain of thought to enhance AI model performance. Learn to optimize test-time compute for improved accuracy and efficiency.
Rustc-Chalk Integration Overview: Plans and Implementation
Overview of rustc-chalk integration plans and implementation, covering answer tables, search strategies, inference contexts, and key components like the trait engine and canonicalization.
Inference of Probabilistic Programs with Moment-Matching Gaussian Mixtures
Explore a novel approach to probabilistic program inference using Gaussian mixtures, offering improved accuracy and efficiency for complex models and distributions.
Variational Autoencoder From Scratch in PyTorch
Learn to implement a Variational Autoencoder from scratch using PyTorch, covering architecture, training loop, and inference with practical examples and code demonstrations.
How to Do Stable Diffusion LORA Training by Using Web UI on Different Models
Comprehensive guide to LoRA training in Stable Diffusion, covering installation, image preparation, parameter tuning, and image generation using SD 1.5 and 2.1 models.
Sparse Is Enough in Scaling Transformers - ML Research Paper Explained
Detailed explanation of Scaling Transformers and Terraformer architecture, focusing on leveraging sparsity to improve efficiency and speed in large language models while maintaining accuracy.
A Visual Introduction to Modern and Scalable Machine Learning Pipeline
Visual guide to modern, scalable machine learning pipelines: from data prep to deployment. Covers key stages like training, validation, inference, feature stores, and model registry. Ideal for understanding ML infrastructure evolution.
Neural Simulation-based Inference - Lecture 5
Explore neural simulation-based inference techniques for high energy physics data analysis, focusing on advanced machine learning applications in particle physics research.
Neural Simulation-based Inference - Lecture 2
Explore advanced neural simulation-based inference techniques for high energy physics, focusing on applications in data analysis and new physics discovery at particle colliders.
Intro and Building a Machine Learning Framework
Learn to build a reusable machine learning framework for tabular datasets, covering setup, training, and inference in a two-part video series.
Differentiable Associative Memories, Attention, and Transformers
Explore differentiable associative memories, attention mechanisms, and transformers. Learn about energy minimization, planning, and various transformer applications in machine learning and AI.