Explore MATLAB source code curated for "深度学习" with clean implementations, documentation, and examples.
Comprehensive code references for deep learning implementations, featuring benchmark programs for CAE, CNN, DBN, NN, SAE, and other fundamental architectures with practical implementation examples and algorithm explanations.
deepLearnToolbox-master is a comprehensive deep learning package for MATLAB containing various machine learning algorithms including Convolutional Neural Networks (CNN), Deep Belief Networks (DBN), and AutoEncoders (Stacked SAE, Convolutional CAE). The package was developed by Rasmus Berg Palm.
Application Background: This code is designed for robotic grasp learning, enabling robots to determine optimal grasping strategies (e.g., grasping a cup by its body vs. handle). It utilizes multimodal data including cups, remote controls, and cameras to train neural networks in grasp decision-making. Implemented in MATLAB with detailed post-extraction instructions. Key Technologies: Includes a comprehensive README file detailing usage procedures and downloadable training datasets. Core training is executed through trainGraspRecMultiSparse.m in recTraining folder, implementing sparse multimodal network architecture for grasp preference learning.
The Deep Learning MATLAB Toolbox serves as an invaluable starting point for newcomers, providing foundational materials that cover essential concepts, algorithm implementations, and practical coding examples to accelerate learning.
Deep learning application using convolutional neural networks for character recognition - a well-structured program implementation.
This is a particle swarm optimization toolbox available for download. Contains implementations for artificial intelligence/neural networks/deep learning applications with modular algorithm structures and configurable parameters.
Complete working code implementation for the stacked autoencoder exercise from Stanford's Deep Learning Tutorial, featuring handwritten digit recognition dataset integration and ready-to-execute neural network architecture
Adaptive Convolutional Network Toolkit – an essential resource for deep learning beginners to study and understand CNN implementation.
Comprehensive approach to image noise reduction utilizing deep neural networks for enhanced image quality preservation.
Neural Network Approach to License Plate Recognition with Technical Implementation Details