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Applications

Enrichir des workflows de Deep Learning avec la Computer Vision, le traitement d'images, la conduite autonome, les signaux, l’audio, l’analyse de texte et la finance computationnelle

Utilisez Deep Learning Toolbox™ pour intégrer le Deep Learning à des applications de Computer Vision, de traitement d'images, de conduite autonome, de traitement du signal, audio, d’analyse de texte et de finance computationnelle.

Exemples présentés

Train Deep Learning Semantic Segmentation Network Using 3-D Simulation Data

Use 3-D simulation data to train a semantic segmentation network and fine-tune it to real-world data using generative adversarial networks (GANs).

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Lidar Object Detection Using Complex-YOLO v4 Network

Detect objects in point clouds using you only look once version 4 (YOLO v4) deep learning network. In this example, you will

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Train Biped Robot to Walk Using Reinforcement Learning Agents

Train a reinforcement learning agent to control a biped walking robot modeled in Simscape™ Multibody™.

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Increase Image Resolution Using Deep Learning

Create a high-resolution image from a low-resolution image using a very-deep super-resolution (VDSR) neural network.

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Chemical Process Fault Detection Using Deep Learning

Use simulation data to train a neural network than can detect faults in a chemical process.

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Compare Deep Learning Networks for Credit Default Prediction

Create, train, and compare three deep learning networks for predicting credit default probability.

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Sequence-to-Sequence Translation Using Attention

Convert decimal strings to Roman numerals using a recurrent sequence-to-sequence encoder-decoder model with attention.

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Classify ECG Signals Using Long Short-Term Memory Networks

Classify heartbeat electrocardiogram data using deep learning and signal processing.

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Waveform Segmentation Using Deep Learning

Segment human electrocardiogram signals using time-frequency analysis and deep learning.

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Applications

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Exemples présentés