Math 0-1 matrix calculus in data science and machine learning
This course starts with an introduction to the key concepts and outlines the roadmap to success in the field. You'll begin by understanding the foundational elements of matrix and vector derivatives, exploring topics like linear and quadratic forms, chain rules in matrix form, and the derivativ...
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| Format: | Elektronisch Video |
| Sprache: | English |
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[Place of publication not identified]
Packt Publishing
2024
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| Ausgabe: | [First edition]. |
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| 520 | |a This course starts with an introduction to the key concepts and outlines the roadmap to success in the field. You'll begin by understanding the foundational elements of matrix and vector derivatives, exploring topics like linear and quadratic forms, chain rules in matrix form, and the derivative of determinants. Each concept is reinforced with exercises, ranging from quadratic challenges to least squares and Gaussian methods. The course progresses into optimization techniques essential in data science and machine learning. Delve into multi-dimensional second derivative tests, gradient descent in one and multiple dimensions, and Newton's method, including practical exercises in Newton's Method for least squares. An additional focus is set on setting up your environment, where you'll learn to establish an Anaconda environment and install crucial tools like Numpy, Scipy, and TensorFlow. The course also addresses effective learning strategies, answering pivotal questions like the suitability of YouTube for learning calculus and the recommended order for taking courses in this field. As you journey through the course, you'll transition from foundational concepts to advanced applications, equipping yourself with the skills needed to excel in data science and machine learning. What you will learn Understand matrix and vector derivatives Master linear and quadratic forms Apply the chain rule in matrix calculus Solve optimization problems using gradient descent and Newton's method Set up the Anaconda environment for machine learning Install and use key libraries like Numpy and TensorFlow Develop effective strategies for learning calculus in data science Audience This course suits students and professionals eager to learn the math behind AI, Data Science, and Machine Learning, ideal for deepening knowledge in these advanced technology fields. Learners should have a basic knowledge of linear algebra, calculus, and Python programming to effectively understand matrix calculus. A keen interest and enthusiasm for exploring this intricate subject are also crucial for a fulfilling learning experience. About the Author Lazy Programmer: The Lazy Programmer, a distinguished online educator, boasts dual master's degrees in computer engineering and statistics, with a decade-long specialization in machine learning, pattern recognition, and deep learning, where he authored pioneering courses. His professional journey includes enhancing online advertising and digital media, notably increasing click-through rates and revenue. As a versatile full-stack software engineer, he excels in Python, Ruby on Rails, C++, and more. His expansive knowledge covers areas like bioinformatics and algorithmic trading, showcasing his diverse skill set. Dedicated to simplifying complex topics, he stands as a pivotal figure in online education, adeptly navigating students through the nuances of data science and AI. | ||
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As you journey through the course, you'll transition from foundational concepts to advanced applications, equipping yourself with the skills needed to excel in data science and machine learning. What you will learn Understand matrix and vector derivatives Master linear and quadratic forms Apply the chain rule in matrix calculus Solve optimization problems using gradient descent and Newton's method Set up the Anaconda environment for machine learning Install and use key libraries like Numpy and TensorFlow Develop effective strategies for learning calculus in data science Audience This course suits students and professionals eager to learn the math behind AI, Data Science, and Machine Learning, ideal for deepening knowledge in these advanced technology fields. Learners should have a basic knowledge of linear algebra, calculus, and Python programming to effectively understand matrix calculus. A keen interest and enthusiasm for exploring this intricate subject are also crucial for a fulfilling learning experience. About the Author Lazy Programmer: The Lazy Programmer, a distinguished online educator, boasts dual master's degrees in computer engineering and statistics, with a decade-long specialization in machine learning, pattern recognition, and deep learning, where he authored pioneering courses. His professional journey includes enhancing online advertising and digital media, notably increasing click-through rates and revenue. As a versatile full-stack software engineer, he excels in Python, Ruby on Rails, C++, and more. His expansive knowledge covers areas like bioinformatics and algorithmic trading, showcasing his diverse skill set. 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| institution | BVB |
| isbn | 9781835886649 1835886647 |
| language | English |
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| spelling | Math 0-1 matrix calculus in data science and machine learning [First edition]. [Place of publication not identified] Packt Publishing 2024 1 online resource (1 video file (6 hr., 17 min.)) sound, color. zweidimensionales bewegtes Bild tdi rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Online resource; title from title details screen (O'Reilly, viewed January 30, 2024) This course starts with an introduction to the key concepts and outlines the roadmap to success in the field. You'll begin by understanding the foundational elements of matrix and vector derivatives, exploring topics like linear and quadratic forms, chain rules in matrix form, and the derivative of determinants. Each concept is reinforced with exercises, ranging from quadratic challenges to least squares and Gaussian methods. The course progresses into optimization techniques essential in data science and machine learning. Delve into multi-dimensional second derivative tests, gradient descent in one and multiple dimensions, and Newton's method, including practical exercises in Newton's Method for least squares. An additional focus is set on setting up your environment, where you'll learn to establish an Anaconda environment and install crucial tools like Numpy, Scipy, and TensorFlow. The course also addresses effective learning strategies, answering pivotal questions like the suitability of YouTube for learning calculus and the recommended order for taking courses in this field. As you journey through the course, you'll transition from foundational concepts to advanced applications, equipping yourself with the skills needed to excel in data science and machine learning. What you will learn Understand matrix and vector derivatives Master linear and quadratic forms Apply the chain rule in matrix calculus Solve optimization problems using gradient descent and Newton's method Set up the Anaconda environment for machine learning Install and use key libraries like Numpy and TensorFlow Develop effective strategies for learning calculus in data science Audience This course suits students and professionals eager to learn the math behind AI, Data Science, and Machine Learning, ideal for deepening knowledge in these advanced technology fields. Learners should have a basic knowledge of linear algebra, calculus, and Python programming to effectively understand matrix calculus. A keen interest and enthusiasm for exploring this intricate subject are also crucial for a fulfilling learning experience. About the Author Lazy Programmer: The Lazy Programmer, a distinguished online educator, boasts dual master's degrees in computer engineering and statistics, with a decade-long specialization in machine learning, pattern recognition, and deep learning, where he authored pioneering courses. His professional journey includes enhancing online advertising and digital media, notably increasing click-through rates and revenue. As a versatile full-stack software engineer, he excels in Python, Ruby on Rails, C++, and more. His expansive knowledge covers areas like bioinformatics and algorithmic trading, showcasing his diverse skill set. Dedicated to simplifying complex topics, he stands as a pivotal figure in online education, adeptly navigating students through the nuances of data science and AI. Machine learning Computer science Mathematics Apprentissage automatique Informatique ; Mathématiques Instructional films Nonfiction films Internet videos Films de formation Films autres que de fiction Vidéos sur Internet Lazy Programmer (Firm), MitwirkendeR ctb Packt Publishing, Verlag pbl TUM01 ZDB-30-ORH TUM_PDA_ORH https://learning.oreilly.com/library/view/-/9781835886649/?ar X:ORHE Aggregator lizenzpflichtig Volltext |
| spellingShingle | Math 0-1 matrix calculus in data science and machine learning Machine learning Computer science Mathematics Apprentissage automatique Informatique ; Mathématiques Instructional films Nonfiction films Internet videos Films de formation Films autres que de fiction Vidéos sur Internet |
| title | Math 0-1 matrix calculus in data science and machine learning |
| title_auth | Math 0-1 matrix calculus in data science and machine learning |
| title_exact_search | Math 0-1 matrix calculus in data science and machine learning |
| title_full | Math 0-1 matrix calculus in data science and machine learning |
| title_fullStr | Math 0-1 matrix calculus in data science and machine learning |
| title_full_unstemmed | Math 0-1 matrix calculus in data science and machine learning |
| title_short | Math 0-1 |
| title_sort | math 0 1 matrix calculus in data science and machine learning |
| title_sub | matrix calculus in data science and machine learning |
| topic | Machine learning Computer science Mathematics Apprentissage automatique Informatique ; Mathématiques Instructional films Nonfiction films Internet videos Films de formation Films autres que de fiction Vidéos sur Internet |
| topic_facet | Machine learning Computer science Mathematics Apprentissage automatique Informatique ; Mathématiques Instructional films Nonfiction films Internet videos Films de formation Films autres que de fiction Vidéos sur Internet |
| url | https://learning.oreilly.com/library/view/-/9781835886649/?ar |
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