Learn Algebra in this full college course. Algebraic concepts are often used in programming.

This course was created by Dr. Linda Green, a lecturer at the University of North Carolina at Chapel Hill. Check out her YouTube channel: https://www.youtube.com/channel/UCkyLJh6hQS1TlhUZxOMjTFw

Chapters:

• (0:00:00) Exponent Rules
• (0:10:14) Simplifying using Exponent Rules
• (0:31:46) Factoring
• (0:45:08) Factoring – Additional Examples
• (0:55:37) Rational Expressions
• (1:15:22) Rational Equations
• (1:37:01) Absolute Value Equations
• (1:42:23) Interval Notation
• (1:49:35) Absolute Value Inequalities
• (1:56:55) Compound Linear Inequalities
• (2:05:59) Polynomial and Rational Inequalities
• (2:16:20) Distance Formula
• (2:20:59) Midpoint Formula
• (2:23:30) Circles: Graphs and Equations
• (2:33:06) Lines: Graphs and Equations
• (2:41:35) Parallel and Perpendicular Lines
• (2:49:05) Functions
• (3:00:53) Toolkit Functions
• (3:08:00) Transformations of Functions
• (3:20:29) Introduction to Quadratic Functions
• (3:33:02) Standard Form and Vertex Form for Quadratic Functions
• (3:37:18) Justification of the Vertex Formula
• (3:41:11) Polynomials
• (3:49:06) Exponential Functions
• (3:56:53) Exponential Function Applications
• (4:08:38) Exponential Functions Interpretations
• (4:18:17) Compound Interest
• (4:29:33) Logarithms: Introduction
• (4:38:15) Log Functions and Their Graphs
• (4:48:59) Combining Logs and Exponents
• (4:53:38) Log Rules
• (5:02:10) Solving Exponential Equations Using Logs
• (5:10:20) Solving Log Equations
• (5:19:27) Doubling Time and Half Life
• (5:35:34) Systems of Linear Equations
• (5:47:36) Distance, Rate, and Time Problems
• (5:53:20) Mixture Problems
• (5:59:48) Rational Functions and Graphs
• (6:13:13) Combining Functions
• (6:17:10) Composition of Functions
• (6:29:32) Inverse Functions

Henry Segerman explores this virtual experience of non-euclidean space, which is a joint work with Vi Hart, Andrea Hawksley and Sabetta Matsumoto.

Papers:

Code:

Tibees delivers a math lesson about logarithms inspired by the legendary painter Bob Ross.

How far can you go with ONLY language modeling?

Can a large enough language model perform NLP task out of the box?

OpenAI take on these and other questions by training a transformer that is an order of magnitude larger than anything that has ever been built before and the results are astounding.

Yannic Kilcher explores.

Paper

Time index:

• 0:00 – Intro & Overview
• 1:20 – Language Models
• 2:45 – Language Modeling Datasets
• 3:20 – Model Size
• 5:35 – Transformer Models
• 7:25 – Fine Tuning
• 10:15 – In-Context Learning
• 17:15 – Start of Experimental Results
• 23:10 – What I think is happening
• 28:50 – Translation
• 33:00 – Commonsense Reasoning
• 37:30 – SuperGLUE
• 40:40 – NLI
• 41:40 – Arithmetic Expressions
• 48:30 – Word Unscrambling
• 50:30 – SAT Analogies
• 52:10 – News Article Generation
• 1:01:10 – Training Set Contamination
https://github.com/openai/gpt-3

2020 is a census year and we take fast, actionable data analytics for granted.

It wasn’t always easy or fast until the advent of a punchcards brought about by a competition.