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Deep Learning Projects with JavaScript

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Deep Learning Projects with JavaScript
Deep Learning Projects with javascript
.MP4, AVC, 200 kbps, 1920x1080 | English, AAC, 160 kbps, 2 Ch | 2h 13m | 524 MB
Instructor: Jakub Konczyk

Learn how to do text sentiment analysis and detect emotions in people's portraits and their voices using TensorFlow.js

In this course, you will through the process of getting started with TensorFlow.



Learn how to do text sentiment analysis and detect emotions in people's portraits and their voices using TensorFlow.js
Getting started with Deep Learning seems overwhelming with so many options to choose from, so you might be wondering where to start, which tools to choose, and how to actually set them up? The good news is that you already have the key tool in front of you: your web browser with a powerful javascript engine inside it. And when you add the TensorFlow.js library to this combo, you can use Deep Learning methods via javascript in no time.
In this course, you will through the process of getting started with TensorFlow.js to detect emotions with a lot of different types of data. You will start by learning how to build a deep learning tool to judge whether a piece of text is positive or negative. Since you will want tangible results quickly, you will use a pre-trained model to do that and include it into your own web application. You will move on to learn how to detect human emotions based only on pictures and voices using pre-trained models as well. Towards the end, you will learn how to modify a pre-trained model to train the emotional detector from scratch using your own data.
By the end of this course you will know how to use Deep Learning models and train your own models from the ground up using javascript and the TensorFlow.js library.
What You Will Learn
Get started with Deep Learning quickly, without installing anything
Use Deep Learning methods in practice on realistic datasets
Get results fast using pre-trained models
Improve your results using transfer learning
Learn when it's a good idea to train your own model from scratch and what do you need to know to do that correctly
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