English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 2h 43m | 0.98 GB
Apply Machine Learning models in real-time in mobile devices with the new and powerful TensorFlow Lite
This complete guide will teach you how to build and deploy Machine Learning models on your mobile device with TensorFlow Lite. You will understand the core architecture of TensorFlow Lite and the inbuilt models that have been optimized for mobiles.
You will learn to implement smart data-intensive behavior, fast, predictive algorithms, and efficient networking capabilities with TensorFlow Lite. You will master the TensorFlow Lite Converter, which converts models to the TensorFlow Lite file format. This course will teach you how to solve real-life problems related to Artificial Intelligence—such as image, text, and voice recognition—by developing models in TensorFlow to make your applications really smart. You will understand what Machine Learning can do for you and your mobile applications in the most efficient way. With the capabilities of TensorFlow Lite you will learn to improve the performance of your mobile application and make it smart.
By the end of the course, you will have learned to implement AI in your mobile applications with TensorFlow.
You will gain an insight into solving real-life problems through Deep Learning using TensorFlow as the main tool for building models that will be later deployed on a mobile device. This course starts with a theoretical introduction and reinforces every concept by a practical code implementation. After a first simplistic example is used to understand the basics, different real-life problems in Computer Vision will deepen your knowledge by walking you through classical steps in developing an app such as identifying challenges, tackling problems, and deploying our ideas.
What You Will Learn
- Learn basic Deep Learning concepts
- Build Deep Learning models in TensorFlow
- Understand the main components of a TensorFlow model
- Debug and improve TensorFlow models
- Deploy TensorFlow models on iOS and Android platforms
- Design solutions to real-life computer vision problems
- Tackle typical challenges when developing real-life applications
Table of Contents
01 The Course Overview
02 Deep Learning
03 Deep Learning Components
04 TensorFlow
05 TensorFlow Lite
06 Hello World in TensorFlow
07 Debugging Our Model
08 Parameter Study
09 Overfitting
10 Deployment in iOS with TensorFlow Lite
11 Introduction to the Problem and Dataset
12 Developing the Handwriting Recognition Model
13 Parameter Study
14 Testing the Model
15 Deployment in Android with TensorFlow Lite
16 Data Augmentation
17 Developing the Pattern Recognition Model
18 Parameter Study and Data Augmentation
19 Testing the Model
20 Deployment in Android with TensorFlow Lite
21 Introduction
22 Developing the Gesture Recognition Model
23 Parameter Study and Data Augmentation
24 Adapting and Debugging the Model
25 Deployment in Android with TensorFlow Lite
26 Introduction
27 Developing the Voice Recognition Model
28 Dropout and Dataset Generation
29 Deployment in Android with TensorFlow Lite
30 Course Summary
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