English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 13m | 529 MB
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.
This course will teach you how to use Deep Learning methods in JavaScript and how to apply them to your own website or web application.
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
Table of Contents
Easy to Use Deep Learning Tools at Your Fingertips
1 The Course Overview
2 What Makes Deep Learning in JavaScript Special
3 Getting Started with TensorFlow.js
Text Sentiment Analysis
4 Loading Pre-Trained CNN and LSTM Models
5 Preparing a New Text for Sentiment Analysis
6 Using Loaded Model for Real-Time Text Analysis
What You See Is What You Get – Photo Emotion Detection
7 Loading a Set of Pre-Trained CNN Models for Emotion Detection in Photos
8 Preparing a New Image for Analysis
9 Using Our Models for Photo Emotion Detection
You Sound Happy – Speech Emotion Detection
10 Loading a Pre-Trained CNN Model for Voice Emotion Detection
11 Preparing a New Audio Sample for Analysis
12 Using the Loaded CNN Model for Detecting Emotions in Speech
Improving Speech Emotion Detection Using Transfer Learning
13 Create a New Model Based on a Pre-Trained CNN Model
14 Getting and Preparing a New Audio Sample for Training and Testing
15 Training and Testing the New Model
Training Speech Emotion Detection Model From Scratch
16 Getting and Preparing Audio Sample
17 Building a CNN Model for Emotion Detection
18 Training and Testing the Model
19 Using Trained CNN Model on New Audio Samples
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