An Introduction to Optimization: With Applications to Machine Learning

Price: $131.95 – $102.99
(as of Jan 04,2025 04:24:29 UTC – Details)

Publisher ‏ : ‎ Wiley; 5th edition (October 10, 2023)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 672 pages
ISBN-10 ‏ : ‎ 1119877636
ISBN-13 ‏ : ‎ 978-1119877639
Item Weight ‏ : ‎ 2.85 pounds
Dimensions ‏ : ‎ 7.3 x 1.6 x 10.2 inches

If you’re new to the world of optimization and its applications in machine learning, you’ve come to the right place. In this post, we’ll provide you with an introduction to optimization and how it plays a crucial role in the field of machine learning.

Optimization is the process of finding the best solution to a problem from a set of possible solutions. In the context of machine learning, optimization is used to find the best set of parameters for a model that minimizes a given objective function, such as the loss function. By optimizing these parameters, we can improve the performance of the model and make more accurate predictions.

There are various optimization techniques that can be used in machine learning, such as gradient descent, stochastic gradient descent, and Adam optimization. These techniques involve iteratively updating the model parameters to minimize the loss function and improve the model’s performance.

Optimization is a fundamental aspect of machine learning, as it allows us to train models efficiently and effectively. By understanding the principles of optimization and how it can be applied to machine learning, you can develop more robust and accurate models for a wide range of applications.

So whether you’re a beginner in the field of machine learning or an experienced practitioner looking to enhance your skills, understanding optimization is key to achieving success in this exciting and rapidly evolving field. Stay tuned for more insights and practical tips on how to optimize your machine learning models for better performance.
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