Optimization Algorithms for Machine Learning: Theory and Practice - Prashad
-20% with code BOOKS
Shipping in 10-16 days
30-day return policy
In the realm of machine learning, optimization algorithms play a pivotal role in refining models for optimal performance. These algorithms, ranging from classic gradient descent to advanced techniques like stochastic gradient descent (SGD), Adam, and RMSprop, are fundamental in minimizing the error function and enhancing model accuracy. Each algorithm offers unique advantages: SGD efficiently handles large ... Full description
You May Also Like
TOP
The Design of Everyday Things
-20% with code BOOKS
In stock at our supplier
TOP
If Anyone Builds It, Everyone Dies: The Case Against Superintelligent AI
Eliezer Yudkowsky, Nate Soares
-20% with code BOOKS
In stock at our supplier
TOP
We Are Not Machines: The Fight for the Future of Work
-20% with code BOOKS
In stock at our supplier
TOP
Open Circuits: The Inner Beauty of Electronic Components
Eric Schlaepfer, Windell Oskay
-20% with code BOOKS
In stock at our supplier
TOP
Get started with MicroPython on Raspberry Pi Pico: The Official Raspberry Pi Pico Guide
-20% with code BOOKS
In stock at our supplier
TOP
Practical Electronics for Inventors
-20% with code BOOKS
In stock at our supplier
TOP
Nucleo Boards Programming with the STM32CubeIDE: Hands-on in more than 50 projects
-20% with code BOOKS
In stock at our supplier
TOP
INCOSE Systems Engineering Handbook
-20% with code BOOKS
In stock at our supplier
TOP
Fundamentals of Aerodynamics ISE
Christopher Cadou, John D. Anderson
-20% with code BOOKS
In stock at our supplier
TOP
Black Holes: The Key to Understanding the Universe
-20% with code BOOKS
In stock at our supplier
TOP
KiCad Like A Pro - Fundamentals and Projects: Getting started with the world's best open-source PCB tool
-20% with code BOOKS
In stock at our supplier
TOP
Make: Electronics: Learning by Discovery: A hands-on primer for the new electronics enthusiast
-20% with code BOOKS
In stock at our supplier
TOP
Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
Steven L. Brunton, J. Nathan Kutz
-20% with code BOOKS
In stock at our supplier
TOP
The Idea Factory: Bell Labs and the Great Age of American Innovation
-20% with code BOOKS
In stock at our supplier
TOP
Flowers For Algernon: A Modern Literary Classic
-20% with code BOOKS
In stock at our supplier
TOP
Foundations of Flavor: The Noma Guide to Fermentation: Including Step-By-Step Information on Making and Cooking with: Koji, Kombuchas, Shoyus, Misos, Vinegars, Garums, Lacto-Ferments and black Fruits Vegetables
-20% with code BOOKS
In stock at our supplier
TOP
Professional SMD Assembly: Practical Low-Cost Methods for Reliable PCB Production
-20% with code BOOKS
In stock at our supplier
TOP
Blood in the Machine: The Origins of the Rebellion Against Big Tech
-20% with code BOOKS
In stock at our supplier
TOP
Mastering the Arduino Uno R4: Programming and Projects for the Minima and WiFi
-20% with code BOOKS
In stock at our supplier
Description
In the realm of machine learning, optimization algorithms play a pivotal role in refining models for optimal performance. These algorithms, ranging from classic gradient descent to advanced techniques like stochastic gradient descent (SGD), Adam, and RMSprop, are fundamental in minimizing the error function and enhancing model accuracy. Each algorithm offers unique advantages: SGD efficiently handles large datasets by updating parameters iteratively, while Adam adapts learning rates dynamically based on gradient variance. Theoretical understanding of optimization algorithms involves comprehending concepts like convexity, convergence criteria, and the impact of learning rate adjustments. Practically, implementing these algorithms requires tuning hyperparameters and balancing computational efficiency with model effectiveness. Moreover, recent advancements such as meta-heuristic algorithms (e.g., genetic algorithms) expand optimization capabilities for complex, non-convex problems. Mastering optimization algorithms equips practitioners with the tools to improve model robustness and scalability across diverse applications, ensuring machine learning systems perform optimally in real-world scenarios.