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Course Outline
MATLAB Deep Learning Environment & GPU Validation
- Deep Learning Toolbox architecture & workflow overview
- Verifying GPU availability, CUDA/cuDNN compatibility, and driver configuration
- Configuring parallel workers, memory management, and gpuArray basics
- Lab 1: Environment validation & running your first GPU-accelerated deep learning script
Core Deep Learning Constructs in MATLAB
- Neural network layers: conv, pooling, batch norm, dropout, residual & dense layers
- dlarray, dlnetwork, and custom training loop fundamentals
- Loss functions, optimizers (Adam, SGD, RMSProp), and learning rate scheduling
- Visualizing architectures, weight distributions, and gradient flow
- Lab 2: Building a custom dlnetwork from scratch and debugging layer interactions
Designing CNNs for Image Recognition
- CNN design patterns: feature extraction, spatial hierarchies, and receptive fields
- Transfer learning: using pre-trained networks (ResNet, EfficientNet, MobileNet)
- Data augmentation pipelines with imageDatastore, augmentedImageDatastore, and custom transforms
- Lab 3: Training a CNN from scratch on a custom image classification dataset with augmentation
Automated Data Labeling & Reproducible Pipelines
- MATLAB’s active learning & semi-supervised labeling tools
- Importing/exporting annotations (COCO, Pascal VOC, YOLO, CSV)
- Building version-controlled, parameterized data preparation scripts
- Lab 4: Automating the labeling workflow and integrating it into a training script
Scalable Training: Multi-GPU, Cloud & Clusters
- Multi-GPU training strategies: batchsize tuning, gradient accumulation, and data parallelism
- Distributed training with MATLAB Parallel Server & on-prem clusters
- Cloud training workflows (AWS, Azure, GCP) via MATLAB cloud compute profiles
- Training monitoring, checkpointing, and hyperparameter optimization
- Lab 5: Scaling a model to multi-GPU/cloud setup and profiling training throughput
Cross-Framework Interoperability & Model Exchange
- Importing pre-trained Caffe & TensorFlow/Keras models into MATLAB
- Validating accuracy parity and adapting architectures for MATLAB workflows
- Exporting models to ONNX, TensorFlow, or Core ML for cross-platform deployment
- Lab 6: Importing a TF-Keras model, fine-tuning it in MATLAB, and exporting to ONNX
Capstone Project & Production Readiness
- End-to-end pipeline: data → training → validation → optimization → deployment
- Model compression: pruning, quantization, and code generation with GPU Coder
- Reproducibility best practices: logging, seeding, and sharing MATLAB deep learning apps
- Capstone: Build, train, optimize, and export a complete image recognition system tailored to your domain
To request a customized course outline for this training, please contact us.
Requirements
- Proficiency in MATLAB (syntax, programming workflows, toolbox familiarity)
- No prior data science or deep learning experience required
- Access to a local GPU-enabled workstation (CUDA-compatible) or approved cloud cluster for live labs
Audience
- Developers & Software Engineers
- Research Engineers & Domain Experts
- Teams transitioning from traditional signal/image processing to AI-driven workflows
14 Hours
Testimonials (3)
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
Nola - Laramie County Community College
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
