Machine learning and optimization methods continue to evolve, finding applications in various fields of science, technology, and business. The book "Innovations in Optimization and Machine Learning" by Toufik Mzili presents a study of modern algorithms, hybrid approaches, and methods for improving model performance. The publication is oriented toward the practical application of optimization strategies for deep learning, data analysis, and artificial intelligence (AI).
This is an advanced guide to optimization methods in AI and ML. It will help you master modern algorithms, enhance model performance, and apply the latest machine learning techniques. Download the "Innovations in Optimization and Machine Learning" book and begin exploring advanced approaches to optimization today!
What are the distinctive features of this publication?
- Modern optimization methods: The guide covers new approaches to improving neural networks, including genetic algorithms, swarm methods, and stochastic optimization.
- Hybrid solutions: Analysis of the combination of traditional optimization algorithms with machine learning for complex problems.
- Practical examples: Working with Python, TensorFlow, PyTorch, and specialized libraries.
- Optimization of deep neural networks: Examination of methods such as adaptive gradient descent, regularization, and automated hyperparameter tuning.
- Current research: Discussion of the latest advancements in heuristic algorithms and their application in machine learning.
What will the book "Innovations in Optimization and Machine Learning" teach you?
- The theoretical foundations and practical application of optimization methods in AI.
- The use of genetic algorithms and evolutionary strategies for model tuning.
- The application of Bayesian optimization and gradient methods in machine learning.
- The enhancement of model performance using quantum algorithms and random search methods.
- Automated neural network architecture selection using AutoML.
- The optimization of model parameters using differential evolution and swarm intelligence methods.
More About the Author of the Book
FAQ for "Innovations in Optimization and Machine Learning"
Is the book suitable for beginners in machine learning?
No, the publication is designed for specialists familiar with the basics of ML and optimization. It is recommended to have experience with Python, TensorFlow, or PyTorch.
Which programming languages are used in this publication?
The main focus is on Python, and tools such as Matlab and Julia for numerical computations are also discussed.
Does the guide cover optimization for deep learning?
Yes, a significant part of the book is dedicated to neural network optimization, including methods of regularization, adaptive gradient descent algorithms, and quantum computing.
Are the methods in the book applicable to real-world problems?
Yes, it includes practical examples for optimizing financial models, medical diagnostics, and image processing.
Does the guide describe modern AutoML tools?
Yes, Toufik Mzili discusses AutoML, Hyperopt, and Optuna for automatic hyperparameter selection.
Information
| Author: | Toufik Mzili | Language: | English |
| Publisher: | Engineering Science Reference | ISBN-13: | 979-8369352311 |
| Publication Date: | September 13, 2024 | ISBN-10: | 836935231A |
| Print Length: | 504 pages | Category: | Machine Learning and Artificial Intelligence Books |
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