Numerical Optimization Techniques in Machine Learning for Electrical Engineers: With Python (Electrical Engineering Essentials with Python)

Book Description:

Unlock the power of numerical optimization in machine learning tailored for electrical engineers. This comprehensive guide traverses through the myriad of optimization techniques, providing you with a solid foundation in leveraging these methods for sophisticated machine learning implementations. With Python code included for every chapter, you will gain practical insights and tools to solve complex optimization challenges across a range of applications, from high-dimensional spaces to edge computing.

Built on the backbone of advanced theories and algorithms, this book demystifies numerical optimization, enabling engineers and practitioners to enhance machine learning models and address real-world constraints efficiently. Whether you are an adept engineer looking to deepen your understanding or a beginner aspiring to integrate machine learning into electrical engineering tasks, this book is your gateway to mastering optimization techniques.

Key Features:

Delve into gradient descent and its variants suitable for high-dimensional parameter spaces.Understand stochastic gradient descent and variance reduction techniques for large-scale problems.Explore Newton’s methods and quasi-Newton alternatives for efficient optimization.Dive into trust region and conjugate gradient methods for nonlinear optimization tasks.Discover leverage KKT conditions for solving constrained optimization problems.Study primal-dual interior point methods for convex optimization challenges.Grasp the nuances of Bayesian optimization for efficient hyperparameter tuning.Unravel optimization strategies for both convex and non-convex landscapes in machine learning.

What You Will Learn:

Master Gradient Descent Methods in high-dimensional spaces.Implement Stochastic Gradient Descent with advanced variance reduction.Employ Newton and Quasi-Newton Methods in optimization problems.Analyze Conjugate Gradient Methods for complex nonlinear problems.Utilize Trust Region Methods for efficient optimization processes.Optimize Nonlinear Least Squares with the Levenberg-Marquardt Algorithm.Apply Karush-Kuhn-Tucker (KKT) Conditions in constrained optimization.Leverage Lagrangian Duality in machine learning applications.Explore Primal-Dual Interior Point Methods for large-scale convex problems.Solve Nonlinear Optimization using Sequential Quadratic Programming (SQP).Master Proximal Gradient Methods for nonsmooth functions.Deploy Alternating Direction Method of Multipliers (ADMM) for convex problems.Exploit Spectral Methods using eigenvalues in optimization contexts.Optimize Support Vector Machines with efficient algorithms.Learn Backpropagation and Automatic Differentiation for neural networks.Apply Regularization Methods: Ridge and Lasso in models.Engage Sparse Optimization and Compressed Sensing for signal processing.Dive into Convex Optimization and Duality Theory fundamentals.Navigate Non-Convex Optimization within Deep Learning networks.Utilize Penalty and Barrier Methods for constraint optimization.Implement Projected Gradient Methods in constrained scenarios.Excel in Stochastic Optimization for online learning environments.Optimize Inference in Probabilistic Graphical Models.Employ Expectation-Maximization Algorithm for complex models.Harness Variational Inference within Bayesian machine learning.

ASIN ‏ : ‎ B0D3G6JZHB
Publication date ‏ : ‎ November 7, 2024
Language ‏ : ‎ English
File size ‏ : ‎ 4405 KB
Text-to-Speech ‏ : ‎ Not enabled
Enhanced typesetting ‏ : ‎ Not Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 403 pages
Format ‏ : ‎ Print Replica