Deep Learning & Machine Learning Masterclass w/ TensorFlowJS

Learn Machine Learning and Deep Learning from scratch using JavaScript and Tensorflow.js with hands-on projects

4.50 (4 reviews)
Udemy
platform
English
language
Data Science
category
Deep Learning & Machine Learning Masterclass w/ TensorFlowJS
122
students
9.5 hours
content
Jan 2021
last update
$54.99
regular price

What you will learn

Get acquainted with machine learning and deep learning capabilities using JavaScript

Understand the JavaScript Machine Learning ecosystem

Know how to decide, analyze, and make predictions from real-world data

Build deep learning models with TensorFlow .js and practice on realistic datasets

Description

Machine learning and Deep Learning have been gaining immense traction lately, but until now JavaScript developers have not been able to take advantage of it due to the steep learning curve involved in learning a new language. Here comes a browser-based JavaScript library, TensorFlow.js to your rescue using which you can train and deploy machine learning models entirely in the browser. If you’re a JavaScript developer who wants to enter the field ML and DL using TensorFlow.js, then this course is for you.

Towards the end of this course, you will be able to implement Machine Learning and Deep Learning for your own projects using JavaScript and the TensorFlow.js library.

This course is project-based so you will not be learning a bunch of useless coding practices. At the end of this course, you will have real-world apps to use in your portfolio. We feel that project-based training content is the best way to get from A to B. Taking this course means that you learn practical, employable skills immediately.

You can use the projects you build in this course to add to your LinkedIn profile. Give your portfolio fuel to take your career to the next level.

Learning how to code is a great way to jump into a new career or enhance your current career. Coding is the new math and learning how to code will propel you forward in any situation. Learn it today and get a head start for tomorrow. People who can master technology will rule the future.

Content

01a (Prerequisite) Introduction to Javascript

01. Course Requirements
02. Html, CSS And Javascript Crash Course
03. Adding In Functions
04. Scaling Functions
05. Changing The Text In Javascript
06. Variables
07. Arrays
08. Objects
09. Variable Scope
10. Adding User Input Text
11. Calling Functions
12. If Statements
13. Else If And Else Statements
14. Changing The Style With Javascript

01c TensorFlow JS Fundamentals

01 What Is Machine Learning
02 What Is Tensorflow JS
03 Load Tensorflow Object
Source Files

01d Build Your First Tensors

00 Linear Algebra For Machine Learning
01 Build Tensors
02 Tensor Utility Methods
03 Perform Math With Tensors
Source Files

01e What is a Neural Network

00A What Is Deep Learning
00B What Is A Neural Network
Source Files

02 Build a Neural Network with One Hot Encoding

00 What Is One Hot Encoding
01 Build Training Data
02 Build The Neural Network
03 Train The Neural Network
04 Make A Prediction
Source Files

03 Build a Neural Network to Detect Lines in Images

01 Build Training Data To Represent Images
02 Build The Convolutional Neural Network
03 Train The Convolutional Neural Network
04 Make A Prediction Of Number Of Lines-4
Source Files

04 Build an LSTM Recurrent Neural Network

00 What Is A Recurrent Neural Network
01 Generate Sequence And Label
02 Generate Dataset
03 Build The LSTM Model
04 Train The Model
Source Files

05 Build a Model to Classify Iris Species

01 Process Iris Data
02 Convert Data To Tensors
03 Separate Training And Testing Data
04 Create Training And Testing Datasets
05 Build The Model
06 Train The Model
07 Make A Prediction
Source Files

06 Build a Positive vs Negative Text Classifier

01 Load Model And Dataset
02 Get User Input For Sentiment Analysis
03 Make A Prediction
Source Files

07 Build a Neural Network to Recognize Handwriting

00 What Is A Convolutional Neural Network
01 Set Up Canvas To Load Image Data
02 Load MNIST Dataset
03 Separate Training And Testing Data
04 Build The Model
04A What Are The Network's Layers
05 Train The Model
06 Create Training Batches
07 Create Testing Batches
08 Fit Neural Network Through Data
Source Files

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3773188
udemy ID
1/13/2021
course created date
1/22/2021
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