Udemy - Basic Numerical Methods - Numerical Analysis for M - c Learning

Basic Numerical Methods: Numerical Analysis for M/c Learning
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Last updated 03/2023
Duration: 2h 42m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 3.02 GB
Genre: eLearning | Language: English[Auto]

Numerical Methods: Learn Basics of Numerical Analysis for Deep learning, Machine Learning , AI and Data Science

What you'll learn
Understand how Numerical Methods fits into the broader context of computer science
Develop a deep understanding of the concepts of numerical analysis
Learn how to interpret formulae and understand practical approach
Learn how to deal with common issues in numerical methods
Requirements
High school knowledge of Math and specially calculus
Description
Numerical methods play a critical role in machine learning, deep learning, artificial intelligence, and data science. These methods are essential for solving complex mathematical problems that are common in these fields.
One of the most important uses of numerical methods in these areas is in the optimization of machine learning models. Optimization is the process of finding the set of model parameters that minimize a given objective function. This process involves complex mathematical calculations that often require numerical methods such as gradient descent, Newton's method, and conjugate gradient methods.
Numerical methods are also used in the analysis of large datasets. Data scientists often encounter datasets that are too large to be processed using traditional methods. In these cases, numerical methods such as randomized linear algebra and Monte Carlo simulations can be used to efficiently process the data.
Another important use of numerical methods in these areas is in the simulation of complex systems. Simulations are used to model the behavior of complex systems such as weather patterns, financial markets, and biological systems. Numerical methods such as finite element methods, spectral methods, and stochastic simulations are essential for accurately simulating these systems.
Here , in this course you'll receive support through a Q&A section, and the course is continually updated based on student feedback, with plans to add new topics in the future.
So why wait? Enroll today and take the first step toward achieving your goals. With the right tools and support, you can make your dreams a reality and achieve the high score you deserve. Don't miss out on this opportunity to excel and boost your confidence.

Who this course is for
Deep learning, Machine Learning Artificial Intelligence and data science students and professional