Large-Scaleデータ Fine tuning of large-scale language models and RLHF (Reinforcement Learning from Human Feedback)
Introduction
Fine tuning of large-scale language models is an additional learning process on models that hav...
Large-Scaleデータ
Large-Scaleデータ Large-Scaleデータ Overview of Federated Learning and various algorithms and implementation examples
Federated Learning
Federated Learning is a new approach to training machine learning models that addresses th...
Large-Scaleデータ Parallel and Distributed Processing in Machine Learning
Parallel and Distributed Processing in Machine Learning
The learning process of machine learning requires hi...
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)
Clojure Protected: Stochastic gradient descent implementation using Clojure and Hadoop
Stochastic gradient descent implementation using Clojure and Hadoop for digital transformation, artificial intelligence, and machine learning tasks (mini-batch, Mapper, Reducer, Parkour, Tesser, batch gradient descent, join-step Partitioning, uberjar, Java, batch gradient descent, stochastic gradient descent, Hadoop cluster, Hadoop distributed file system, HDFS)
Clojure Protected: Clojure implementation of distributed computation processing (map-reduce) used in Hadoop
Clojure implementation of distributed computation processing (map-reduce) used in Hadoop for digital transformation, artificial intelligence, and machine learning tasks Tesser, Reducer function, fold, cost function, gradient descent method, feature extraction, feature-scales function, feature scaling, gradient descent learning rate, gradient descent update rule, iterative algorithm, multiple regression, correlation matrix, fuse, commutative, linear regression, co-reduction, and covariance) feature-scales function, feature scaling, gradient descent learning rate, gradient descent update rule, iterative algorithm, multiple regression, correlation matrix, fuse, commutativity, linear regression, covariance, Hadoop, pararrel fold