Familiarization with MATLAB ® AI toolboxes - Versionsgeschichte

Moye-nyuysoni.glein-perry@stud.hshl.de: /* 2. During MATLAB Installation */

2025-04-11T23:37:50Z

2. During MATLAB Installation

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	Step 1: When installing MATLAB from the MathWorks installer, select the toolboxes you need from the available list.		Step 1: When installing MATLAB from the MathWorks installer, select the toolboxes you need from the available list.
	Step 2: Complete the installation process. (This method ensures that the selected toolboxes are integrated with your MATLAB installation.)		Step 2: Complete the installation process. (This method ensures that the selected toolboxes are integrated with your MATLAB installation.)

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			→ zurück zum Hauptartikel: [[Signalverarbeitung_mit_MATLAB_und_Künstlicher_Intelligenz\| Signalverarbeitung mit MATLAB und Künstlicher_Intelligenz]]

Moye-nyuysoni.glein-perry@stud.hshl.de am 18. März 2025 um 23:20 Uhr

2025-03-18T23:20:33Z

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	\| '''Betreuer''': \|\| [[Benutzer:Ulrich_Schneider\| Prof. Dr.-Ing. Schneider]]		\| '''Betreuer''': \|\| [[Benutzer:Ulrich_Schneider\| Prof. Dr.-Ing. Schneider]]
	\|}		\|}
	[[Datei:Images.png\|~~frameless~~\|~~right~~\|~~\|500px~~\|alternativtext=Matlab AI Image\|Matlab AI]]		[[Datei:Images.png\|thumb\|rigth\|700px\|alternativtext=Matlab AI Image\|Matlab AI]]
	== Introductioin ==		== 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 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.

Moye-nyuysoni.glein-perry@stud.hshl.de am 17. März 2025 um 12:04 Uhr

2025-03-17T12:04:41Z

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	\| '''Betreuer''': \|\| [[Benutzer:Ulrich_Schneider\| Prof. Dr.-Ing. Schneider]]		\| '''Betreuer''': \|\| [[Benutzer:Ulrich_Schneider\| Prof. Dr.-Ing. Schneider]]
	\|}		\|}
	[[Datei:Images.png\|frameless\|right\|alternativtext=Matlab AI Image\|Matlab AI]]		[[Datei:Images.png\|frameless\|right\|\|500px\|alternativtext=Matlab AI Image\|Matlab AI]]
	== Introductioin ==		== 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 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.

Moye-nyuysoni.glein-perry@stud.hshl.de am 17. März 2025 um 12:02 Uhr

2025-03-17T12:02:46Z

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			\| '''Autor:''' \|\| [mailto:moye-nyuysoni.glein-perry@stud.hshl.de Moye Nyuysoni Glein Perry]
			\|-
			\| '''Art:''' \|\| Project Work
			\|-
			\| '''Starttermin''': \|\| 14.11.2024
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			\| '''Abgabetermin''': \|\| 31.03.2025
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			\| '''Betreuer''': \|\| [[Benutzer:Ulrich_Schneider\| Prof. Dr.-Ing. Schneider]]
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	[[Datei:Images.png\|frameless\|right\|alternativtext=Matlab AI Image\|Matlab AI]]		[[Datei:Images.png\|frameless\|right\|alternativtext=Matlab AI Image\|Matlab AI]]
	== Introductioin ==		== Introductioin ==

Moye-nyuysoni.glein-perry@stud.hshl.de am 17. März 2025 um 12:01 Uhr

2025-03-17T12:01:33Z

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	Integration with Pretrained Networks: Easily incorporate pretrained deep learning models for image classification and segmentation tasks.		Integration with Pretrained Networks: Easily incorporate pretrained deep learning models for image classification and segmentation tasks.

			https://de.mathworks.com/products/computer-vision.html
	=== 4. Reinforcement Learning Toolbox ===		=== 4. Reinforcement Learning Toolbox ===
	===== Description: =====		===== Description: =====
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	Resources:		Resources:

	~~Reinforcement Learning Toolbox Overview~~		https://de.mathworks.com/products/reinforcement-learning.html
	~~Reinforcement Learning Toolbox Documentation~~
	=== 5. Text Analytics Toolbox ===		=== 5. Text Analytics Toolbox ===
	===== Description: =====		===== Description: =====
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	Resources:		Resources:

	~~Text Analytics Toolbox Overview~~		https://de.mathworks.com/products/text-analytics.html
	~~Text Analytics Toolbox Documentation~~
	== Verifying Toolbox Installation ==		== Verifying Toolbox Installation ==
	To check which toolboxes are installed, you can use the following methods:		To check which toolboxes are installed, you can use the following methods:

Moye-nyuysoni.glein-perry@stud.hshl.de am 17. März 2025 um 11:55 Uhr

2025-03-17T11:55:40Z

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 https://de.mathworks.com/products/deep-learning.html
-=== 2. Statistics and Machine Learning Toolbox ===
+=== 2.STATISTICS AND MACHINE LEARNING TOOLBOX ===
 ===== Description: =====
 This toolbox provides a suite of algorithms and functions for statistical analysis, machine learning, and predictive analytics. It is fundamental for developing models that classify, regress, cluster, and perform dimensionality reduction.
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 *Feature Engineering: Methods for feature selection and extraction.
 == Verifying Toolbox Installation ==
 To check which toolboxes are installed, you can use the following methods:

Moye-nyuysoni.glein-perry@stud.hshl.de am 17. März 2025 um 11:39 Uhr

2025-03-17T11:39:50Z

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	=== 1. DEEP LEARNING TOOLBOX ===		=== 1. DEEP LEARNING TOOLBOX ===
			===== Description: =====
	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.		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.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:		===== Key Features: =====
	* Custom Architectures: Create, train, and fine-tune custom network architectures.		* Custom Architectures: Create, train, and fine-tune custom network architectures.
	* Visualization & Analysis: Tools for network visualization, layer analysis, and debugging.		* Visualization & Analysis: Tools for network visualization, layer analysis, and debugging.
	* Hardware Acceleration: Support for GPU acceleration and integration with hardware like CUDA-enabled devices.		* Hardware Acceleration: Support for GPU acceleration and integration with hardware like CUDA-enabled devices.
	* Interoperability: Import models from frameworks such as TensorFlow and ONNX.		* Interoperability: Import models from frameworks such as TensorFlow and ONNX.
	https://de.mathworks.com/products/deep-learning.html		https://de.mathworks.com/products/deep-learning.html

			=== 2. Statistics and Machine Learning Toolbox ===
			===== Description: =====
			This toolbox provides a suite of algorithms and functions for statistical analysis, machine learning, and predictive analytics. It is fundamental for developing models that classify, regress, cluster, and perform dimensionality reduction.
			===== Key Features: =====

			*Algorithm Variety: Implements classification, regression, clustering, and ensemble methods.
			*Data Analysis: Tools for hypothesis testing, data visualization, and model validation.
			Feature Engineering: Methods for feature selection and extraction.Algorithm Variety: Implements classification, regression, clustering, and ensemble methods.
			*Data Analysis: Tools for hypothesis testing, data visualization, and model validation.
			*Feature Engineering: Methods for feature selection and extraction.


	== Verifying Toolbox Installation ==		== Verifying Toolbox Installation ==

Moye-nyuysoni.glein-perry@stud.hshl.de am 17. März 2025 um 11:29 Uhr

2025-03-17T11:29:06Z

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			[[Datei:Images.png\|frameless\|right\|alternativtext=Matlab AI Image\|Matlab AI]]
	== Introductioin ==		== 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 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.

Moye-nyuysoni.glein-perry@stud.hshl.de am 17. März 2025 um 11:22 Uhr

2025-03-17T11:22:01Z

@@ Zeile 10: / Zeile 10: @@
 *	Integrating AI with system-level simulations to prevent production errors
-== 1. DEEP LEARNING TOOLBOX ==
+''Some of the key toolboxes used in AI and machine learning workflows include:''
 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.
@@ Zeile 20: / Zeile 38: @@
 *	Interoperability: Import models from frameworks such as TensorFlow and ONNX.
 https://de.mathworks.com/products/deep-learning.html

Moye-nyuysoni.glein-perry@stud.hshl.de: /* Introductioin */

2025-03-17T10:59:42Z

Introductioin

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	''MATLAB provides several AI capabilities, including:''		''MATLAB provides several AI capabilities, including:''

	* Building AI models using ~~minimal~~ code or using pre-trained models		* Building AI models using few lines of code or using pre-trained models
			* Facilitating seamless AI model and design integration between MATLAB and Python
	* Utilizing specialized tools and low-code applications to create scalable AI workflows		* Utilizing specialized tools and low-code applications to create scalable AI workflows
			* Deploying AI models on high-performance platforms like edge devices and the cloud
	* Integrating AI with system-level simulations to prevent production errors		* 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 ==		== 1. DEEP LEARNING TOOLBOX ==