Artificial Intelligence - TensorFlow Machine Learning

Name: Artificial Intelligence - TensorFlow Machine Learning
Rating: 3.7 (24 reviews)

AI and Machine Learning from theory to the required hands-on experience by using latest TensorFlow JavaScript framework

Created byVinay Phadnis

Last updated 1/2019

English

What you'll learn

Understand Machine Learning from basics
Understand the basics of a neural network
Understand basics of Tensor operations for Tensorflow
Create project using Polynomial Regression
Create basic HTML, Javascript projects
Use JSFiddle to create Javascript applets
Use NodeJs to create comprehensive machine learning projects

Course content

11 sections • 70 lectures • 9h 21m total length

What is Machine Learning or AI6:28
why browser or Javascript3:40
Course Guidelines3:17

Section Intro1:23
Integrating with HTML10:14
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.
Variables and Variable types10:16
Conditioning8:44
Functions()10:20
Looping in Javascript11:13
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.

What is a tensor5:51
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.
Creating a tensor10:24
Tensorflow zeros5:32
Tensorflow Variables4:43
Operations of variables6:42
Project #22:48
Solution #25:35
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.

Install node and npm0:07
Project Description5:26
Apply machine learning to reveal patterns in randomly generated data points and train a model to fit a curve using a loss function.
npm init5:58
Creating the input dataset - 114:32
Creating the input dataset - 28:15
Building a model7:22
Loss and train Function11:09
Building User Interface (Part -1)10:40
Building User Interface (Part -2)6:24
Creating the html16:53
Styling the HTML14:48
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.
Completing index.js15:44
Adding Babel javascript10:15
package.json0:03
Installing Yarn0:16
Running the Program!7:04

Genetics6:01
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 Population7:47
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.
Activate Brain7:45
Evolve Population11:52
Selection10:34
Mutating Genes11:47
Finishing Genetic.js4:23

Creating a Bird Object11:23
Creating a Tree Object11:35
Creating a Tree Group12:29
Creating a Bird Group11:21
Bitmap Objects12:17
Adding Buttons9:12
Update switch case11:36
Add Collisions11:28
Reset the Background10:56
More Methods11:41
Completing the Code!11:55
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.
Running the Project8:30
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.

Requirements

Interest about Machine Learning
Wanting to learn Artificial Intelligence on the world's most used platform
Interest about Tensorflow

Description

This course teaches machine learning from the basics so that you can get started with created amazing machine learning programs. With a well structured architecture, this course is divided into 4 modules:

Theory section: It is very important to understand the reason of learning something. The need for learning machine learning and javascript in this particular case is explained in this section.
Foundation section: In this section, most of the basic topics required to approach a particular problem are covered like the basics of javascript, what are neural networks, dom manipulation, what are tensors and many more such topics
Practice section: In this section, you put your learnt skills to a test by writing code to solve a particular problem. The explanation of the solution to the problem is also provided in good detail which makes hands-on learning even more efficient.
Project section: In this section, we build together a full stack project which has some real life use case and can provide a glimpse on the value creation by writing good quality machine learning programs

Happy Coding,

Vinay Phadnis :)

Who this course is for:

Beginners: Getting started with Tensorflow
Intermediates: Learn by having an integrated platform
Experts: Further polishing your skills

Artificial Intelligence - TensorFlow Machine Learning

What you'll learn

Explore related topics

Course content

Introduction3 lectures • 13min

Javascript Basics6 lectures • 52min

DOM manipulations4 lectures • 30min

Getting started with Machine Learning7 lectures • 42min

Equation Finder16 lectures • 2hr 15min

Getting Started with Neural Networks4 lectures • 26min

Build your own Neural Network3 lectures • 29min

Flappy Bird Game6 lectures • 35min

Setting up Neural Network for Flappy Bird7 lectures • 1hr

Back to Gameplay.js12 lectures • 2hr 14min

Requirements

Description

Who this course is for: