DX

Clojure

Protected: Large-scale Machine Learning with Apache Spark and MLlib

Large-scale machine learning with Apache Spark and MLlib for digital transformation, artificial intelligence, and machine learning tasks (predictive value, RMSE, factor matrix, rank, latent features, neighborhoods, sum of squares error, Mahout, ALS, Scala RDD, alternating least squares, alternating least squares, stochastic gradient descent, persistence, caching, Flambo, Clojure, Java)
python

Protected: the application of neural networks to reinforcement learning(1) overview

Overview of the application of neural networks to reinforcement learning utilized in digital transformation, artificial intelligence and machine learning tasks (Agent, Epsilon-Greedy method, Trainer, Observer, Logger, Stochastic Gradient Descent, Stochastic Gradient Descent, SGD, Adaptive Moment Estimation, Adam, Optimizer, Error Back Propagation Method, Backpropagation, Gradient, Activation Function Stochastic Gradient Descent, SGD, Adaptive Moment Estimation, Adam, Optimizer, Error Back Propagation, Backpropagation, Gradient, Activation Function, Batch Method, Value Function, Strategy)
IOT技術:IOT Technology

Introduction and configuration of Apache Spark for distributed data processing

Deployment and configuration of Apache Spark to enable distributed data processing for digital transformation, artificial intelligence and machine learning tasks (NodeManager, YARN, spark-master, ResourceManager, spark-worker, HDFS, NameNode, DataNode, spark-client, CDH5.4, haddop, Yum, CentOS) spark-worker, HDFS, NameNode, DataNode, spark-client, CDH5.4, haddop, Yum, CentOS)
アーキテクチャ

Deploying and Operating Microservices – Docker and Kubernetes

Deployment and operation of microservices leveraged for digital transformation, artificial intelligence and machine learning tasks - Docker and Kubernetes minikube, containers, deployment, kube-ctl, rolling-upgrade, auto-bin packing, horizontal scaling, scale-up, scale-down, self-healing, kubelet, kube-apiserver, etcd, kube-controller- manager, kube- scheduler, pod, kube-proxy, Docker CLI, the Docker Registry, cgroups, Linux kernel, kernel namespace, union mount option, Hypervisor
アルゴリズム:Algorithms

Machine Learning by Ensemble Methods – Fundamentals and Algorithms Reading Notes

Fundamentals and algorithms in machine learning with ensemble methods used in digital transformation, artificial intelligence and machine learning tasks class unbalanced learning, cost-aware learning, active learning, semi-supervised learning, similarity-based methods, clustering ensemble methods, graph-based methods, festival label-based methods, transformation-based methods, clustering, optimization-based pruning, ensemble pruning, join methods, bagging, boosting
アルゴリズム:Algorithms

Protected: Information Geometry of Positive Definite Matrices (3)Calculation Procedure and Curvature

Procedures and curvature of computation of positive definite matrices as informative geometry utilized in digital transformation, artificial intelligence, and machine learning tasks
アルゴリズム:Algorithms

Protected: Measures for Stochastic Banded Problems Likelihood-based measures (UCB and MED measures)

Measures for Stochastic Banded Problems Likelihood-based UCB and MED measures (Indexed Maximum Empirical Divergence policy, KL-UCB measures, DMED measures, Riglet upper bound, Bernoulli distribution, Large Deviation Principle, Deterministic Minimum Empirical Divergence policy, Newton's method, KL divergence, Binsker's inequality, Heffding's inequality, Chernoff-Heffding inequality, Upper Confidence Bound)
アルゴリズム:Algorithms

Protected: Online Stochastic Optimization and Stochastic Gradient Descent for Machine Learning

Stochastic optimization and stochastic gradient descent methods for machine learning for digital transformation DX, artificial intelligence AI and machine learning ML task utilization
アルゴリズム:Algorithms

Protected: Optimality conditions and algorithm stopping conditions in machine learning

Optimality conditions and algorithm stopping conditions in machine learning used in digital transformation, artificial intelligence, and machine learning scaling, influence, machine epsilon, algorithm stopping conditions, iterative methods, convex optimal solutions, constrained optimization problems, global optimal solutions, local optimal solutions, convex functions, second order sufficient conditions, second order necessary conditions, first order necessary conditions
アルゴリズム:Algorithms

Protected: Unsupervised Learning with Gaussian Processes (2) Extension of Gaussian Process Latent Variable Model

Extension of Gaussian process latent variable models as unsupervised learning by Gaussian processes, an application of stochastic generative models utilized in digital transformation, artificial intelligence, and machine learningtasks ,infinite warp mixture models, Gaussian process dynamics models, Poisson point processes, log Gaussian Cox processes, latent Gaussian processes, elliptic slice sampling
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