Flujos de trabajo de IA de extremo a extremo
Utilice Deep Learning Toolbox™ en flujos de trabajo de extremo a extremo que incluyen la definición de requisitos, la preparación de datos, el entrenamiento de redes neuronales profundas, la compresión, las pruebas y verificación de redes, la integración de Simulink y el despliegue.
Temas
- Estimación del estado de carga de una batería utilizando deep learning
Defina los requisitos, prepare los datos, entrene las redes de deep learning, verifique su solidez, integre redes en Simulink y despliegue modelos. (Desde R2024b)
- PASO 1: Define Requirements for Battery State of Charge Estimation
- PASO 2: Prepare Data for Battery State of Charge Estimation Using Deep Learning
- PASO 3: Train Deep Learning Network for Battery State of Charge Estimation
- PASO 4: Compress Deep Learning Network for Battery State of Charge Estimation
- PASO 5: Test Deep Learning Network for Battery State of Charge Estimation
- PASO 6: Integrate AI Model into Simulink for Battery State of Charge Estimation
- PASO 7: Generate Code for Battery State of Charge Estimation Using Deep Learning
- Train and Compress AI Model for Road Damage Detection
Train and compress a sequence classification network using pruning, projection, and quantization to meet a fixed memory requirement. (Desde R2025a)
- PASO 1: Train Sequence Classification Network for Road Damage Detection
- PASO 2: Compress Sequence Classification Network for Road Damage Detection
- PASO 3: Tune Compression Parameters for Sequence Classification Network for Road Damage Detection
- PASO 4: Generate Simulink Model from Sequence Classification Network for Road Damage Detection
- Verify an Airborne Deep Learning System
This example shows how to verify a deep learning system for airborne applications and is based on the work in [5,6,7], which includes the development and verification activities required by DO-178C [1], ARP4754A [2], and prospective EASA and FAA guidelines [3,4]. (Desde R2023b)
Ejemplos destacados
Field-Oriented Control of PMSM Using Position Estimated by Neural Network
Implement field-oriented control (FOC) of a permanent magnet synchronous motor (PMSM) using a rotor position estimated by an autoregressive neural network (ARNN) trained with Deep Learning Toolbox™.
