Building Machine Learning Solutions with TensorFlow.js 2

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Abhishek Kumar

4:08:11

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01. Course Overview

1. Course Overview.mp4

01:53

02. Introduction

1. Version Check.mp4

00:16

2. Introduction.mp4

03:43

3. Why TensorFlow.js-.mp4

04:13

4. TensorFlow.js Performance.mp4

02:58

5. TensorFlow.js Overview.mp4

03:20

6. Course Demo.mp4

04:00

7. Course Structure.mp4

02:26

03. Setting up TensorFlow.js Environment

1. Introduction.mp4

01:28

2. TensorFlow.js in Browser Using Script Tag.mp4

00:58

3. Demo- Running TensorFlow.js in Browser with Script Tag.mp4

05:48

4. TensorFlow.js in Browser Using Package Managers.mp4

02:45

5. Demo- Running TensorFlow.js in Browser Using NPM and Parcel.mp4

07:30

6. Demo- Exploring TensorFlow.js Backends.mp4

06:16

7. Demo- Running TensorFlow.js in Node.js.mp4

05:24

8. Summary.mp4

01:21

04. Understanding TensorFlow.js Core Concepts

1. Introduction.mp4

01:58

2. Tensor Overview.mp4

04:00

3. Demo- Working with Tensors.mp4

02:33

4. Basic Tensor Operations.mp4

01:21

5. Demo- Performing Basic Tensor Operations.mp4

04:31

6. Managing Memory with TensorFlow.js.mp4

03:29

7. Demo- Managing Memory with TensorFlow.js.mp4

01:59

8. Summary.mp4

01:54

05. Preparing Data for Machine Learning Model- Part 1

1. Introduction.mp4

01:37

2. Machine Learning Workflow.mp4

01:50

3. Toxicity Detection Use Case.mp4

03:59

4. Working with TFJS Data.mp4

02:46

5. Async JS Programming.mp4

06:08

6. Demo- Reading Data Using TFJS Data.mp4

08:22

7. Working with TFVis.mp4

01:29

8. Demo- Visualizing Data Using TFVis.mp4

05:13

9. Summary.mp4

01:38

06. Preparing Data for Machine Learning Model- Part 2

1. Introduction.mp4

01:44

2. Generating Features from Text.mp4

05:39

3. Demo- Generating TFIDF Features.mp4

10:45

4. Function Generator.mp4

02:04

5. Demo- Creating Feature Dataset Using Generators.mp4

02:23

6. Train Validation Test Split.mp4

02:03

7. Demo- Splitting Data into Train Validation and Test Datasets.mp4

03:58

8. Summary.mp4

01:23

07. Building, Training, and Evaluating Machine Learning Model

01. Introduction.mp4

01:54

02. Neural Network Overview.mp4

04:01

03. Building Neural Network Using Layers API.mp4

02:38

04. Demo- Building Neural Network Using Layers API.mp4

03:04

05. Training Model Using TensorFlow.js.mp4

03:41

06. Demo- Training Neural Network Model.mp4

03:24

07. Demo- Visualizing Training Performance.mp4

02:17

08. Demo- Evaluating Model Performance.mp4

02:19

09. Model Performance Metrics.mp4

02:03

10. Demo- Visualizing Model Performance Metrics.mp4

03:42

11. Demo- Running Training in Node.js.mp4

02:35

12. Summary.mp4

01:31

08. Saving and Loading Machine Learning Model

1. Introduction.mp4

01:00

2. Model Export Options.mp4

01:53

3. Demo- Exporting Trained Model.mp4

03:11

4. Load Model.mp4

01:11

5. Demo- Loading Trained TensorFlow.js Model.mp4

01:14

6. Summary.mp4

01:13

09. Predicting Using Trained Machine Learning Model

1. Introduction.mp4

01:26

2. Model Scoring.mp4

01:46

3. Demo- Predicting Using Trained TensorFlow.js Model.mp4

03:56

4. Materialize UI.mp4

01:03

5. Demo- Setting up Materialize UI.mp4

04:47

6. Demo- Integrate Steps with UI.mp4

10:35

7. TensorFlow.js Converter.mp4

01:29

8. Demo- Predicting Using Python Exported Model.mp4

09:49

9. Summary.mp4

01:36

10. Using Pre-trained Models with TensorFlow.js

1. Introduction.mp4

01:26

2. Transfer Learning with TensorFlow.js.mp4

04:08

3. Demo- Creating Features from Universal Sentence Encoder (USE) Model.mp4

05:31

4. Demo- Performing Transfer Learning on USE Encoded Features.mp4

04:38

5. Toxicity Detection Model.mp4

02:50

6. Demo- Using TensorFlow.js Toxicity Detection Model.mp4

05:03

7. Summary.mp4

01:07

11. Whats Next-

1. Taking Your Journey Forward.mp4

05:05

Description

In this course, you'll learn to use TensorFlow.js to build, train, and deploy machine learning and deep learning models to power client-side and server-side applications using the JavaScript language.

What You'll Learn?

Machine learning and deep learning are powering some of the most groundbreaking applications of the current era. However, up until recently, JavaScript was not considered the go-to language for machine learning model development and deployment, despite being one of the most popular languages in the world. TensorFlow.js now allows JavaScript developers to extend their skills to build, train, and deploy machine learning and deep learning models. In this course, Building Machine Learning Solutions with TensorFlow.js 2, you'll learn about the TensorFlow.js ecosystem and how to set it up on the client-side in the browser and on the server-side with Node.js. First, you'll discover how to use the environment to build an end-to-end machine learning application that uses natural language processing (NLP) under the hood to detect toxic elements in unstructured text. Next, you'll learn how to import and process data, build, train, and export a model, and finally predict using the trained model. Finally, you'll explore how to use existing models trained in Python on the client-side using TensorFlow.js, and even retrain the pre-trained model using transfer learning. By the end of this course, you'll have the skills and knowledge of TensorFlow.js to build, train, and deploy machine learning and deep learning models on the client-side, as well as on the server-side that can power sophisticated applications.

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Machine Learning

TensorFlow

Abhishek Kumar

Instructor's Courses

Abhishek Kumar is a data science consultant, author, and Google Developers Expert (GDE) in machine learning. He holds a master’s degree from the University of California, Berkeley, and has been featured in the "Top 40 under 40 Data Scientist" list. He is also a public speaker and has delivered talks in top data conferences across the globe including Strata Data, AI conference, ODSC, and Fifth Elephant. His focus area is machine learning and deep learning at scale and is also a recipient of the "Hal Varian" award for his work on deep learning at UC Berkeley. Abhishek has worked on various machine learning and deep learning projects involving recommender systems, image recognition, forecasting, optimization, anomaly detection, and natural language processing.

Pluralsight

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Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.