Deep Learning

Deep learning is a sub-set of a machine learning in which the algorithms inspired by neural networks and human brain learn from the data.

With just a few lines of MATLAB code, you can apply deep learning techniques to your work whether you’re designing algorithms, preparing and labeling data, or generating code and deploying to embedded systems

With MATLAB, you can: 

  • Create, modify, and analyze deep learning architectures using apps and visualization tools.
  • Preprocess data and automate ground-truth labeling of image, video, and audio data using apps.
  • Accelerate algorithms on NVIDIA GPUs, cloud, and datacenter resources without specialized programming.
  • Collaborate with peers using frameworks like TensorFlow, PyTorch, and MxNet
  • Simulate and train dynamic system behavior with reinforcement learning
  • Generate simulation-based training and test data from MATLAB and Simulink models of physical systems.

We will show you three approaches to train a deep learning network:

  • Training a network from the scratch
  • Using the transmission learning to train an available network
  • Training an available network for semantic segmentation

Practical Deep Learning Examples with MATLAB

Predictive Maintenance Analytics with MATLAB e-book

Semantic segmentation, object detection, and image recognition. Computer vision applications integrated with deep learning provide advanced algorithms with deep learning accuracy. MATLAB® provides an environment to design, create, and integrate deep learning models with computer vision applications

You can easily get started with specialized functionality for computer vision such as: 

  • Image and video labeling apps
  • Image datastore to handle large amounts of data for training, testing, and validation
  • Image and computer vision-specific preprocessing techniques
  • Ability to import deep learning models from TensorFlow™ -Keras and PyTorch for image recognition

Prepare and Label Image, Time-Series, and Text Data

MATLAB significantly reduces the time required to preprocess and label data sets for audio, video image, and text data. Synchronize disparate time series, replace outliers with interpolated values, deblur images, and filter noisy signals. Use interactive apps to label, crop, and identify important features, and built-in algorithms to help automate the process of labeling.

Artificial Intelligence and Deep Learning with GPU’s e-book

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