End-to-End AI Workflows
Use Deep Learning Toolbox™ in end-to-end workflows that include defining requirements, data preparation, deep neural training, compression, network testing and verification, Simulink integration, and deployment.
Topics
- Battery State of Charge Estimation Using Deep Learning
Define requirements, prepare data, train deep learning networks, verify robustness, integrate networks into Simulink, and deploy models. (Since R2024b)
- STEP 1: Define Requirements for Battery State of Charge Estimation
- STEP 2: Prepare Data for Battery State of Charge Estimation Using Deep Learning
- STEP 3: Train Deep Learning Network for Battery State of Charge Estimation
- STEP 4: Compress Deep Learning Network for Battery State of Charge Estimation
- STEP 5: Test Deep Learning Network for Battery State of Charge Estimation
- STEP 6: Integrate AI Model into Simulink for Battery State of Charge Estimation
- STEP 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. (Since R2025a)
- STEP 1: Train Sequence Classification Network for Road Damage Detection
- STEP 2: Compress Sequence Classification Network for Road Damage Detection
- STEP 3: Tune Compression Parameters for Sequence Classification Network for Road Damage Detection
- STEP 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]. (Since R2023b)
Featured Examples
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™.
