Optimization For Machine Learning Finding Function Optima With Python

For many engineering problems we require optimization processes with dynamic adaptation as we aim to establish the dimension of the search space where the optimum solution resides and develop robust techniques to avoid the local optima usually associated with multimodal problems. This book explores multidimensional particle swarm optimization, a technique developed by the authors that addresses these requirements in a well-defined algorithmic approach.

Learn grid and random search, Bayesian optimization, multi-fidelity models, Optuna, Hyperopt, Scikit-Optimize & more.

Learn the approaches and tools to tune hyperparameters and improve the performance of your machine learning models.

Learn the approaches and tools to tune hyperparameters and improve the performance of your machine learning models.

Learn Machine learning and data science with python and solve real world machine learning problems

Learn Importance of Machine Learning With All Libraries Like Numpy, Pandas, Matplotlib, Sklearn

This book is intended to present the state of the art in research on machine learning and big data analytics. The accepted chapters covered many themes including artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with big data. It is a valuable resource for researchers in the area of big data analytics and its applications.

This book is intended to present the state of the art in research on machine learning and big data analytics. The accepted chapters covered many themes including artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with big data. It is a valuable resource for researchers in the area of big data analytics and its applications.