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Master calculus for machine learning with this comprehensive online course, covering key concepts like derivatives and integral calculus tailored for AI applications.
Enhance your understanding of machine learning with this comprehensive online course focusing on calculus concepts tailored for artificial intelligence applications. Explore the foundational topics that underpin modern machine learning techniques, starting with Calculus I, where you'll delve into the key concept of limits, including practical exercises on calculating limits that are crucial for understanding how derivatives arise. The course progresses to Differential Calculus, a core component in machine learning, offering insights into derivative rules, power rules, and differentiation processes that help optimize algorithms.
Expand your knowledge with Automatic Differentiation, a pivotal topic for implementing efficient machine learning models, utilizing PyTorch and TensorFlow to streamline calculations. Transition seamlessly into Calculus II, focusing on partial derivatives. Engage in hands-on exercises designed to solidify your grasp of how partial derivatives function, particularly how they relate to gradient descent and optimizing cost functions such as mean squared error.
Integral calculus rounds out the course, presenting tools to master numeric integration and integral calculus rules, which play a crucial role in evaluating models using techniques like the ROC curve, an essential measure for binary classification tasks. This course offers a robust foundation in calculus tailored specifically for machine learning, equipping you with the skills needed to understand and apply mathematical concepts to real-world AI problems effectively.
Video class: Calculus I: Limits
0h03m
Video class: Intro to Differential Calculus —Topic 42 of Machine Learning Foundations
0h13m
Video class: Intro to Integral Calculus –Topic 43 of Machine Learning Foundations
0h02m
Video class: The Method of Exhaustion – Topic 44 of Machine Learning Foundations
0h06m
Video class: Calculus of the Infinitesimals – Topic 45 of Machine Learning Foundations
0h10m
Video class: Calculus Applications – Topic 46 of Machine Learning Foundations
0h08m
Video class: Calculating Limits – Topic 47 of Machine Learning Foundations
0h18m
Video class: Exercises on Limits – Topic 48 of Machine Learning Foundations
0h01m
Video class: Derivatives and Differentiation — Segment 2 of Subject 3, "Limits
0h01m
Video class: The Delta Method – Topic 49 of Machine Learning Foundations
0h15m
Video class: How Derivatives Arise from Limits – Topic 50 of Machine Learning Foundations
0h14m
Video class: Derivative Notation — Topic 51 of Machine Learning Foundations
0h04m
Video class: The Derivative of a Constant –Topic 52 of Machine Learning Foundations
0h01m
Video class: The Power Rule for Derivatives – Topic 53 of Machine Learning Foundations
0h01m
Video class: The Constant Multiple Rule for Derivatives — Topic 54 of Machine Learning Foundations
0h03m
Video class: The Sum Rule for Derivatives — Topic 55 of Machine Learning Foundations
0h02m
Video class: Exercises on Derivative Rules — Topic 56 of Machine Learning Foundations
0h03m
Video class: The Product Rule for Derivatives — Topic 57 of Machine Learning Foundations
0h03m
Video class: The Quotient Rule for Derivatives — Topic 58 of Machine Learning Foundations
0h04m
Video class: The Chain Rule for Derivatives — Topic 59 of Machine Learning Foundations
0h06m
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7 hours and 33 minutes of online video course
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