This book presents a comprehensive coverage of machine learning with Julia, covering from the mathematical foundations to practical applications of various advanced algorithms. Sample codes in Julia are provided to allow readers to implement and improve existing algorithms easily. In this book, the readers will learn how to build machine learning models using Julia's rich ecosystem of libraries, including Flux.jl, MLJ.jl, and more. The readers will explore different types of machine learning approaches, such as supervised learning, unsupervised learning, and reinforcement learning, and learn…mehr
This book presents a comprehensive coverage of machine learning with Julia, covering from the mathematical foundations to practical applications of various advanced algorithms. Sample codes in Julia are provided to allow readers to implement and improve existing algorithms easily. In this book, the readers will learn how to build machine learning models using Julia's rich ecosystem of libraries, including Flux.jl, MLJ.jl, and more. The readers will explore different types of machine learning approaches, such as supervised learning, unsupervised learning, and reinforcement learning, and learn how to implement algorithms such as DBSCAN, self-organizing maps, stochastic neighbor embedding, random forests, and deep learning models. The readers will also learn how to evaluate and interpret machine learning models and how to optimize their performance. Whether readers are a beginner or an experienced data scientist, this book will provide with a solid foundation in machine learning with Julia. By the end of this book, the readers will have the knowledge and skills to tackle real-world machine learning problems using Julia, and the readers will be ready to build intelligent systems that can learn from data, draw insights and make predictions.
Produktdetails
Produktdetails
Machine Learning: Foundations, Methodologies, and Applications
Jeremiah D. Deng is an associate professor in School of Computing at University of Otago, New Zealand. His research interests include pattern recognition, machine learning, and stochastic optimization. He has published at top-tier venues such as PR, NN, TC, TEC, TKDE, TBE, and IJCAI, and serves on the editorial boards of Pattern Analysis and Applications (Springer) and ICT Express (Elsevier) and on the program committees of various AI conferences. Dr. Deng completed his PhD in computer science at University of Hong Kong and South China University of Technology, and has held visiting and adjunct positions at University of Adelaide and South China University of Technology. He is a Senior Member of both IEEE and ACM.
Inhaltsangabe
Introduction.- Metrics and Divergences.- Clustering.- Online Clustering.- Dimension Reduction.- Bayesian classification.- Support Vector Machines = Linear Machines + Kernels.- Tree and Forest: Divide-and-Conquer.- Regression and Model Selection.- Ensemble Methods.- Neural networks.- Convolutional neural networks.- Autoencoders.- Generative adversarial networks.- Transfer Learning.- Federated Learning.
