Protected: Convolutional Neural Networks (2) CNN Overview and Implementation
Overview of convolutional neural networks, a deep learning technique used for image recognition.
Protected: Convolutional Neural Networks (1) Forward and Reverse Propagation Algorithms and Mini-Batch
Forward and back propagation algorithms and mini-batches as the basis for algorithms in DNNs that can be used for digital transformation and artificial intelligence tasks.
Deep Learning Technologies
Deep learning technology, one of the breakthroughs in artificial intelligence technology and machine learning technology
Overview of Kernel Methods and Support Vector Machines
On kernel methods, one of the breakthroughs in machine learning technology
Protected: Classification (4) Group learning(Ensemble Learning, Random Forest) and evaluation of learning results(Cross-validation method)
Algorithms for collective learning for data classification and evaluation of classification results (ensemble learning, bagging, boosting, random forests, cross-validation)
Protected: Classification (3) Probabilistic Discriminant Function(Logistic, Softmax Regression) and Local Learning(K-nearest neighbor method, kernel density estimation)
Probabilistic discriminant functions and local learning used in classifiers for data classification
Protected: Classification (2) Optimization process(Gradient Descent Method, Newton’s Method, Perceptron, SVM)
Classification techniques for image recognition for use in digital transformation (DX) and artificial intelligence (AI), perceptron, squared loss (Adakine algorithm), SVM (SGD-SVM algorithm)
Protected: Classification (1) Algorithm of the classifier(Bayes Decision Rule)
Overview of Classification Algorithms for Image Recognition Used in Digital Transformation (DX) and Artificial Intelligence (AI) (Probabilistic Approach)
Protected: Coding and pooling (BoVW、GMM)
Specific methods of coding and pooling to integrate local features of image recognition into vectors that can be used for digital transformation (DX) and artificial intelligence (AI) (BoVW, mixture Gaussian distribution (GMM), GMM supervector, Fisher vector, VLAD, VLAT).
Statistical Feature Extraction(PCA,LDA,PCS,CCA)
Statistical feature extraction (Principal Component Analysis (PCA), Whitening, Fisher Linear Discriminant Analysis (LDA), Canonical Correlation Analysis (CCA), Partial Least Squares (PLS)) for robust local feature extraction in image recognition used in digital transformation (DX) and artificial intelligence (AI).