Quick Linear Regression vs AI Lab

In statistics, linear regression is a technique for estimating the relationship between an independent variable, X, and its scalar result, the dependent variable, Y, derived from a series of X-Y relationships. The computational routine involves trying to fit a straight line between a scatter plot of X-Y coordinates such that the sum of the squared differences between each dependent outcome, Yi, and the line computed at each Xi is minimized. This is the so-called "ordinary least squares (OLS)" estimator. R-squared, often called the "coefficient of determination," expresses the "goodness of fit" of the line to the scatter plot. It ranges from 0, no fit whatever, to 1, which is a perfect relationship where all the points in the plot fit along the straight line. The iPad version allows you to add, retrieve, reorder, or delete data sets. It also permits emailing, text messaging, and printing of a PDF report. Also, changing the form of the regression equation from Y = function(X) to X = function(Y) is allowed along with estimation of the dependent variable amount from a user-specified independent variable amount.

AI Lab teaches real machine learning from the ground up — no black boxes, no TensorFlow. Build a neural network one neuron at a time, watch it learn in 3-D, then deploy the trained model onto a simulated Raspberry Pi and wire real circuits with Python. AI education you can actually see and touch. Most "learn AI" apps hand you slides and quizzes. AI Lab hands you the engine itself — every neuron, weight, and gradient step computed by a small, deterministic algorithm written from scratch, not a wrapper around someone else's library. Watch a network train, fail, and improve in real time, then shrink it down and run it on real embedded logic. Start with the fundamentals: what a neuron is, how weights, bias, and activation work together, and how one neuron draws a decision line. Then step into Learning by Falling — the Loss Valley lab renders your model's error as a 3-D landscape, and you watch gradient descent roll downhill with every learning-rate tweak. Move on to Deeper Minds: stack hidden layers and watch a network wrap a boundary around a spiral no straight line could solve. Once a model works, AI Lab teaches what most courses skip: making it small enough to run. The Shrinker lab quantizes a network down to a fraction of its size; The Chip lab runs real int8 inference so you feel the accuracy-versus-size-versus-speed tradeoff every embedded engineer faces. Then head to the Raspberry Pi Workbench: wire virtual components and write real Python with gpiozero on a simulated 3-D Pi Pico — blink an LED, sequence a traffic light, fade with PWM, read a button, sweep a five-LED Knight Rider bar. No hardware to buy, no board to brick. WHAT YOU CAN DO • Learn neural networks from first principles — neurons, weights, bias, activation, loss, and gradient descent, before you write code. • Watch training happen in 3-D — the Loss Valley lab turns gradient descent into a landscape you rotate and explore. • Solve what a straight line can't — the Decision Surface lab wraps a boundary around a spiral, with presets from beginner to advanced. • Shrink a real model — quantize a trained network down and see exactly what you traded away. • Run real inference — deploy an int8 model onto a simulated chip and balance accuracy against size and speed. • Wire and code a Raspberry Pi — build circuits on a simulated Pi Pico with real Python and gpiozero, in a built-in editor. • Work through five starter electronics projects, from a first blinking LED to a five-LED Knight Rider sweep. • Earn XP, level up, and build a streak, with a Model Zoo of achievements to unlock. • Everything in one place — Learn, Labs, Pi, and Awards tabs. • Study offline, at your own pace — every lesson, lab, and project lives on your device. HONEST ABOUT WHAT IT IS The neural network engine is a real, working implementation built for learning and visualization — small enough to train instantly and stay fully explainable, not a production machine-learning framework. The Raspberry Pi here is a detailed 3-D simulation for safe, hardware-free learning: no component to buy, no board to risk, and the Python you write carries over if you build with a real Pi later. FREE & PRO AI Lab is free to start: the first World of neuron and gradient-descent lessons, the Loss Valley lab, and your first Raspberry Pi projects cost nothing. AI Lab Pro unlocks the full curriculum — every World, every 3-D lab including Decision Surface, Shrinker, and The Chip, every Pi Workbench project, and an ad-free experience. Choose monthly or annual; the annual plan includes a free trial and is the better value long-term. Subscriptions renew automatically unless canceled; manage or cancel anytime in your store account. Neurons to gradients to a working circuit — AI Lab is the fastest way to actually understand AI and embedded electronics, not just read about them. Terms of Use https://www.apple.com/legal/internet-services/itunes/dev/stdeula/ Privacy Policy https://coddykit.com/terms/privacy-policy