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Duration 14 hours
Course Outline
MATLAB Deep Learning Environment & GPU Validation
- Overview of Deep Learning Toolbox architecture and workflows
- Checking GPU availability, CUDA/cuDNN compatibility, and driver settings
- Setting up parallel workers, managing memory, and exploring
gpuArrayfundamentals - Lab 1: Verifying the environment and executing your first GPU-accelerated deep learning script
Core Deep Learning Constructs in MATLAB
- Exploring neural network layers: conv, pooling, batch norm, dropout, residual, and dense
- Essentials of
dlarray,dlnetwork, and implementing custom training loops - Understanding loss functions, optimizers (Adam, SGD, RMSProp), and learning rate scheduling
- Visualizing network architecture, weight distributions, and gradient flow for debugging
- Lab 2: Creating a custom
dlnetworkfrom the ground up and troubleshooting layer interactions
Designing CNNs for Image Recognition
- CNN design principles: feature extraction, spatial hierarchies, and receptive fields
- Transfer learning: utilizing pre-trained networks like ResNet, EfficientNet, and MobileNet
- Building 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
- Utilizing MATLAB’s active learning and semi-supervised labeling capabilities
- Importing and exporting annotations in formats like COCO, Pascal VOC, YOLO, and CSV
- Developing 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 tactics: adjusting batch size, gradient accumulation, and data parallelism
- Distributed training using MATLAB Parallel Server and local clusters
- Cloud training workflows (AWS, Azure, GCP) via MATLAB cloud compute profiles
- Monitoring training, checkpointing, and hyperparameter optimization methods
- Lab 5: Scaling a model to a multi-GPU/cloud setup and profiling training throughput
Cross-Framework Interoperability & Model Exchange
- Importing pre-trained Caffe and TensorFlow/Keras models into MATLAB
- Ensuring accuracy consistency 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 ingestion, training, validation, optimization, and deployment
- Model compression: pruning, quantization, and code generation with GPU Coder
- Best practices for reproducibility: logging, seeding, and sharing MATLAB deep learning apps
- Capstone: Build, train, optimize, and export a complete image recognition system tailored to your specific domain
To request a customized course outline for this training, please contact us.
Requirements
- Solid proficiency in MATLAB, including syntax, programming workflows, and familiarity with toolboxes
- No prior background in data science or deep learning is necessary
- Access to a GPU-enabled workstation (CUDA-compatible) or an approved cloud cluster for practical labs
Audience
- Developers & Software Engineers
- Research Engineers & Domain Experts
- Teams shifting from conventional signal/image processing to AI-driven solutions
Testimonials (2)
Getting people that never used AI some repetition in prompting and people that do use AI to consider different methods to using it.
Matthew Gay - Tarsus Pharmaceuticals
Course - Artificial Intelligence (AI) Overview
The training was organized and well-planned out, and I come out of it with systematized knowledge and a good look at topics we looked at
