Artificial Intelligence - TensorFlow Machine Learning

Explore JavaScript fundamentals, including variables, conditional loops, and comparison operators, and learn how TensorFlow operations integrate with JavaScript concepts for building AI applications.

Learn to display machine learning outputs in a web interface using html and javascript, build an html skeleton, and link external scripts while loading scripts at the bottom.

Explain how to declare and work with basic JavaScript variable types, including numbers, strings, arrays, and objects, and demonstrate simple operations and console output.

conditioning in javascript is introduced, contrasting assignment (=) with comparisons (==, !=, ===, >, <, >=, <=), exploring booleans true/false, and combining conditions with and, or, not.

This lecture teaches defining and calling javascript functions with function keyword, name, and parameters, returning values, and using console.log to display output while applying the do not repeat yourself principle.

Master looping in JavaScript by using for loops with i from 0 to 10 to compute squares, and apply forEach to arrays, while loop considerations, and infinite loop awareness.

Explore javascript techniques using variables and loops to modify images and deliver results for users. Learn front-end and back-end roles, triggering image updates with button actions for machine learning tasks.

Explore the document object model using JavaScript to select and modify page elements by id and class, update text content, and use query selector for multiple elements.

Learn to use querySelectorAll to select and manipulate headings. Iterate with forEach to change text and target elements by tag or class for interactive demos.

Add a button and handle its on click event with JavaScript, updating a counter and headings on the page through DOM selectors and a simple function.

Define a tensor as an arrangement of numbers and compare it to a matrix with a 2 by 3 shape, illustrating addition and the different rules for multiplication.

Write JavaScript to build your first stage, manage links across the content delivery network, and debug in the browser console while linking files and configuring assets.

Learn how to declare and manipulate zero-valued tensors in TensorFlow, covering shapes, matrices, scalars, and alternative ways to represent and read answers.

declare and initialize variables, assign an initial set of numbers, and update values through assignment to reflect new data stored in memory for fast access.

Learn basic tensor and matrix operations, including addition, subtraction, and multiplication, with proper initialization and dimension handling, and apply these concepts to a polynomial regression project using TensorFlow.

In project 2 of the TensorFlow machine learning course, build a density flow based function that takes x and computes x^2 + b x + c using constants, compare outputs.

build a function to process an input by squaring it, adding a coefficient times the input, and returning the final output; the walkthrough shows 19 for input 2.

Apply machine learning to reveal patterns in randomly generated data points and train a model to fit a curve using a loss function.

initialize a new npm project with npm init, name it equation finder, and define description, entry point, tests, repository, keywords, and license for publishing.

Install and save dependencies with npm, import TensorFlow, and generate a random input dataset. Build a polynomial model using coefficients a, b, c, and d for training.

Create a clean input dataset by generating points with noise, calibrate dispersion with sigma, and normalize y to a range of 0 to 1.

Initialize random coefficients for a polynomial model, import data, and define a predictor function that maps x to y; generate data from a file to train the model.

Define a loss function that squares prediction errors and averages them, then train a model using stochastic gradient descent to minimize loss over 75 iterations at a 0.5 learning rate.

Build a web-based user interface for the question finder project, defining HTML structure, CSS styling, and three graphs showing initial, random coefficients, and a final overlaid graph linked by JavaScript.

Learn to style a basic user interface with css: use class and id selectors, apply flex layout and margins, structure data sections and captions, and link a separate stylesheet.

Create a unified script that displays model outputs by wiring data through Vega-Lite, mapping x and y values to a JSON spec, and rendering a chart on an HTML page.

Explore styling the HTML to visualize machine learning results by plotting data points and a prediction line, and render the coefficients in the user interface.

Implement a learn coefficients function that combines data generation, coefficient handling, plotting initial points and predictions, and iterative training to render final coefficients and graphs.

Explore how Babel converts browser-incompatible JavaScript, including import statements and map functions, into browser-friendly code by configuring a .babelrc and setting up a Babel workflow.

Install and initialize the project, run the watch script to start a local server. Resolve a syntax error and study a polynomial regression project's graphs and coefficients.

Explore the basic building block of modern neural networks—the neuron—a small input gateway that processes multiple inputs into outputs, inspired by nerve cells and linking neurons.

Explain how a simple neuron with on/off outputs combines weighted inputs, and uses the rule that if the sum is greater than one the output becomes one, otherwise zero.

Learn how bias directs neuron decisions by adding a constant to inputs, shaping outputs via the activation function in neural networks, and how weights and bias interact to determine outputs.

Explore activation functions and how neural networks propagate forward from inputs to outputs, using nonlinear and sigmoid functions with bias and thresholds.

Explore xor logic project in the TensorFlow machine learning course by building a sequential neural network, adding dense layers with sigmoid activation, and training with a defined optimizer.

Launch a training loop that defines a loss (mean square error) and an optimizer, prepares training data, and iteratively updates the model to improve performance.

Run a TensorFlow machine learning model with two dense layers, an optimizer, and loss to train on binary input data, observing asynchronous execution and decreasing loss.

Engage in an interactive project that evolves a game through generations, selection, and crossover, using an input layer to decide to flap or not, built in javascript.

Set up the workspace, create project folders, and organize JavaScript files into gameplay and genetics, then configure neural network tooling for the AI-driven game.

Import and initialize game assets by loading sprite sheets, images, and target graphics into a canvas, define main and game states, and prepare for in-game rendering.

Set up a Phaser arcade game by preloading bitmap assets, configuring the create function, and applying gravity along the y axis (2000) to drive game physics.

Set up and run a genetic algorithm to evolve a population of birds using crossover on top genes and initialize a neural network evaluation.

Create a random initial population for a neural network brain, resetting any existing population and filling it with units defined by input, hidden, and output parameters for fitness-based evolution.

Implement and explain an activate function that passes a bullet and target into a neural network, normalizes inputs, computes differences, and decides to flap based on the output.

Apply a genetic algorithm to evolve a population by selecting winners via fitness and top players, then generate offspring through crossover and mutation.

Mutate offspring, create a new unit, update the population, and select the top units by fitness through sorting and slicing the best performers.

Explore genetic algorithms in neural networks by implementing crossover and mutation to swap biases, mutate genes, and evolve offspring for diverse network configurations.

Finish implementing Genetic.js by adding random generation, min-max range handling, and a normalize function for positive outputs, and learn to pick random units and map values within bounds.

Create a bird object by configuring its sprite, position (x, y), and index, add a flap animation, enable physics, and implement prototype methods including a restart function.

Create a tree object by extending the border guard prototype with flap and death functions, adjusting velocity and alpha. Integrate text objects, physics, and grouping in Phaser.

Regroup a set of trees into a single group using a phaser-based prototype. Configure positions, velocities, and resets with x, y, height, and gap logic to spawn and recycle trees.

Create a bird group with a genetic algorithm, add birds and barriers, define a target point, and implement scrolling ground and map data in Phaser.

Render a large bitmap map and overlay dynamic text for birds by placing text objects with x/y coordinates, alignment, and looping through birds to show population, fitness, and score.

Add and wire up game buttons, including restart and pause, with on-click listeners and x and y coordinates to drive interactive gameplay and UI flow.

Implement an update function run each frame that uses a switch case to manage initialization and starting states, guiding a genetic algorithm through populations and generations.

Implement a genetic algorithm to evolve bird agents in a Flappy Bird game, managing population, generations, target points, and state updates to identify winners.

Learn to implement physics-based gameplay elements, including distance calculations, collision handling, lighting updates, and dynamic scoring, through step-by-step coding of targets and events.

Develop and debug the flappy bird ai by implementing update logic, distance calculations, and game state transitions. Compute fitness across the population, manage pause and resume, and draw status.

Complete the code by implementing a drawing function that clears and renders a bitmap with game elements, colors, coordinates, and neuron activation visuals for each board.

Launch and debug the project in a browser, using console and inspect tools to fix syntax errors and misnamed variables. Observe the learning bird run via the genetic algorithm.