Deep Learning Toolbox: Comprehensive Implementation Guide
A versatile deep learning toolbox featuring implementations of CAE, CNN, DBN, NN, SAE architectures along with essential utility functions for model training and optimization
CNN - Deep Learning Neural Networks
Convolutional Neural Networks (CNN) - Implementation and Architecture
CNN, DBN for Image Classification
Implementing image recognition using deep learning models
MATLAB Source Code for CNN Convolutional Neural Network Implementation
MATLAB implementation of Convolutional Neural Network (CNN) with detailed source code, including layer configurations and training algorithms
Convolutional Neural Network (CNN) Algorithm for Deep Learning Implementation
This program implements the Convolutional Neural Network (CNN) algorithm for deep learning, featuring separate training and testing modules with comprehensive functionality for model development and evaluation.
High-Quality Code References for Deep Learning
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.
MATLAB Code Implementation for Deep Learning Toolbox
MATLAB programs for deep learning toolbox featuring various algorithms including Neural Networks (NN), Convolutional Neural Networks (CNN), Autoencoders (CAE), Sparse Autoencoders (SAE), and Deep Belief Networks (DBN)
Implementation of Adaptive Neural Network CNN in MATLAB
Implementation of Adaptive Neural Network CNN using MATLAB with code integration
Heart Sound Classification
Cardiac Cycle Segmentation Combined with CNN Implementation
CNN Convolutional Neural Network for Handwritten Digit Recognition
This MATLAB implementation of a CNN convolutional neural network achieves approximately 50% accuracy in handwritten digit recognition, with cnet_tool.m serving as the main demonstration file showcasing dataset loading, model training, and performance evaluation processes.