A learning curve is a graphical representation of the relationship between how proficient people are at a task and the amount of experience they have. Proficiency (measured on the vertical axis) usually increases with increased experience (the horizontal axis), that is to say, the more someone, groups, companies or industries perform a task, the better their performance at the task. The common expression "a steep learning curve" is a misnomer suggesting that an activity is difficult to learn and that expending much effort does not increase proficiency by much, although a learning curve with a steep start actually represents rapid progress. In fact, the gradient of the curve has nothing to do with the overall difficulty of an activity, but expresses the expected rate of change of learning speed over time. An activity that it is easy to learn the basics of, but difficulty to gain proficiency in, may be described as having "a steep learning curve". Learning curve may refer to a specific task or a body of knowledge. Hermann Ebbinghaus first described the learning curve in 1885 in the field of the psychology of learning, although the name did not come into use until 1903. In 1936 Theodore Paul Wright described the effect of learning on production costs in the aircraft industry. This form, in which unit cost is plotted against total production, is sometimes called an experience curve. (Wikipedia).
If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.
From playlist Machine Learning
The intuitive idea of a function
Learning Objectives: Express the idea of a function as an "instruction", a "graph" and a "machine" that take inputs and spit out outputs. However there are constraints: every input must have a corresponding output, and more specifically just ONE corresponding output. ********************
From playlist Discrete Math (Full Course: Sets, Logic, Proofs, Probability, Graph Theory, etc)
Introduction (3): Supervised Learning
Basics of supervised learning; regression, classification
From playlist cs273a
(ML 9.2) Linear regression - Definition & Motivation
Linear regression arises naturally from a sequence of simple choices: discriminative model, Gaussian distributions, and linear functions. A playlist of these Machine Learning videos is available here: http://www.youtube.com/view_play_list?p=D0F06AA0D2E8FFBA
From playlist Machine Learning
Machine Learning by Andrew Ng [Coursera] 06-01 Advice for applying machine learning
From playlist Machine Learning by Professor Andrew Ng
Deep Learning Lecture 2.2 - Linear Least Squares
Deep Learning Lecture - Estimator Theory - Linear least squares (LLS) as an example - Learning problem and loss function - Parameter estimation - Closed-form solution for LLS
From playlist Deep Learning Lecture
Formal Definition of a Function using the Cartesian Product
Learning Objectives: In this video we give a formal definition of a function, one of the most foundation concepts in mathematics. We build this definition out of set theory. **************************************************** YOUR TURN! Learning math requires more than just watching vid
From playlist Discrete Math (Full Course: Sets, Logic, Proofs, Probability, Graph Theory, etc)
Basic form of a linear regression model; mean squared error loss; learning as optimization
From playlist cs273a
How to pick a machine learning model 3: Choosing a loss function
Part of the End-to-End Machine Learning School course library at http://e2eml.school See these concepts used in an End to End Machine Learning project: https://end-to-end-machine-learning.teachable.com/p/polynomial-regression-optimization/ Watch the rest of the How to Choose a Model serie
From playlist E2EML 171. How to Choose Model
What Integral Calculus Is — Topic 85 of Machine Learning Foundations
#MLFoundations #Calculus #MachineLearning This video is an introduction to the integral branch of calculus with a focus on why it works, its characteristic notation, and its applications to machine learning. There are eight subjects covered comprehensively in the ML Foundations series a
From playlist Calculus for Machine Learning
Austin Lawson (7/15/20): A canonical framework for summarizing persistence diagrams
Title: Persistence curves: A canonical framework for summarizing persistence diagrams Abstract: As Topological Data Analysis (TDA) grows in popularity so too does the need for topological methods compatible with modern machine learning algorithms. The use of machine learning algorithms di
From playlist AATRN 2020
Learn Excel from MrExcel - Create A Bell Curve in Excel - Podcast #1663
Last January, in Episode #1507, Bill took a look at generating Random Numbers around a Standard Deviation using " =NORM.INV [Normal Inverse]. Today, in Episode #1663 the Question from Gary is: "How do we create a Bell Curve in Microsoft Excel?". Follow along with Bill as he shows us how to
From playlist MrExcel Top 15
A-Level Maths Edexcel Core 3 Past Paper Questions - Differentiation(3)
Powered by https://www.numerise.com/ All past paper questions for core 3 using differentiation to find the equations of normals and tangents. www.hegartymaths.com http://www.hegartymaths.com/
From playlist Core 3 Edexcel Maths Past Paper Exam Solutions By Topic
The Phillips Curve- Macro Topic 5.2
Hey students. In this video I show you how to draw and shift the Phillips curve. Remember that there are two curves: the short-run Phillips curve and the long-run Phillips curve. The best way to understand them is to remember what you learned in Unit 3 about aggregate demand and supply.
From playlist Macro Unit 5: Long-Run Consequences of Stabilization Policies
Surfaces and Partial Derivatives
Free ebook http://tinyurl.com/EngMathYT Lecture on the mathematics of surfaces and partial derivatives. Levels curves are discussed and how to use them to sketch surfaces of functions. Many examples are discussed on how to calculate partial derivatives. Such ideas aer seen in university
From playlist A second course in university calculus.
How good is my model? Explained by a Data Scientist!
AUC for ROC, Precision-Recall Curves - EXPLAINED! We are going to code out AUC logic for ROC curves and Precision Recall Curves. SPONSOR Kite is a free AI-powered coding assistant that will help you code faster and smarter. The Kite plugin integrates with all the top editors and IDEs to g
From playlist Machine Learning Tips
ROC Curves | Applied Machine Learning, Part 2
Use ROC curves to assess classification models. ROC curves plot the true positive rate vs. the false positive rate for different values of a threshold. This video walks through several examples that illustrate broadly what ROC curves are and why you’d use them. It also outlines interesti
From playlist Applied Machine Learning
Thank you for watching my econ videos. In an AP or introductory college microeconomic course you must draw, shift, and explain a bunch of graphs, including: supply and demand, perfect competition, monopoly, monopolistic competition, monopsony, externalities and more. In this video I explai
From playlist Micro Unit 6: Market Failure and the Government
In this video, you’ll learn more about the evolution of machine learning and its impact on daily life. Visit https://www.gcflearnfree.org/thenow/what-is-machine-learning/1/ for our text-based lesson. This video includes information on: • How machine learning works • How machine learning i
From playlist Machine Learning