Familiarization with MATLAB ® AI toolboxes: Unterschied zwischen den Versionen
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== 1. DEEP LEARNING TOOLBOX == | == 1. DEEP LEARNING TOOLBOX == | ||
Formerly known as the Neural Network Toolbox, the Deep Learning Toolbox provides algorithms, network layers, pretrained models, and apps to design, train, analyze and simulate deep neural networks. | |||
It provides a framework which can be used to create many types of networks like CNNs. It offers the possibility to visualise and interpret network predictions very the network properties and compress neural networks.With the Deep Network Designer app, you can design, modify, and analyze networks, import pre-trained models, and export networks to Simulink. The toolbox also supports integration with other deep learning frameworks, allowing you to import models from PyTorch®, TensorFlow™, and ONNX™ for inference, transfer learning, simulation, and deployment. Additionally, you can export models to TensorFlow and ONNX. | |||
It provides a framework which can be used to create many types of networks like CNNs. It offers the possibility to visualise and interpret network predictions very the network properties and compress neural networks. | |||
Key Features: | Key Features: | ||
Version vom 17. März 2025, 11:31 Uhr
Introductioin
MATLAB offers a lot of tools designed for developing artificial intelligence applications. From deep learning and computer vision to reinforcement learning and text analytics. In addition, MATLAB provides a collection of pretrained networks that help start projects, particularly in image and signal processing.
MATLAB provides several AI capabilities, including:
- Building AI models using minimal code or using pre-trained models
- Utilizing specialized tools and low-code applications to create scalable AI workflows
- Integrating AI with system-level simulations to prevent production errors
- Deploying AI models on high-performance platforms like edge devices and the cloud
- Facilitating seamless AI model and design integration between MATLAB and Python
1. DEEP LEARNING TOOLBOX
Formerly known as the Neural Network Toolbox, the Deep Learning Toolbox provides algorithms, network layers, pretrained models, and apps to design, train, analyze and simulate deep neural networks. It provides a framework which can be used to create many types of networks like CNNs. It offers the possibility to visualise and interpret network predictions very the network properties and compress neural networks.With the Deep Network Designer app, you can design, modify, and analyze networks, import pre-trained models, and export networks to Simulink. The toolbox also supports integration with other deep learning frameworks, allowing you to import models from PyTorch®, TensorFlow™, and ONNX™ for inference, transfer learning, simulation, and deployment. Additionally, you can export models to TensorFlow and ONNX.
Key Features:
- Custom Architectures: Create, train, and fine-tune custom network architectures.
- Visualization & Analysis: Tools for network visualization, layer analysis, and debugging.
- Hardware Acceleration: Support for GPU acceleration and integration with hardware like CUDA-enabled devices.
- Interoperability: Import models from frameworks such as TensorFlow and ONNX.