by Juan Miguel Valverde Martinez | Released March 2018 | ISBN: 9781788990677

Apply Machine Learning models in real-time in mobile devices with the new and powerful TensorFlow LiteAbout This VideoUnderstand the main theory behind Deep Learning and how to apply it in practiceGain practical knowledge by coding TensorFlow models to solve real-life problems such as gesture or voice recognitionLearn how to deploy TensorFlow models on mobile devicesIn DetailThis 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.The code bundle for this video course is available at https://github.com/PacktPublishing/Hands-on-Tensorflow-Lite-for-Intelligent-Mobile-Apps Show and hide more Publisher Resources Download Example Code

1. Chapter 1 : Introduction to Deep Learning
   • The Course Overview 00:03:50
   • Deep Learning 00:04:24
   • Deep Learning Components 00:04:35
   • TensorFlow 00:05:03
   • TensorFlow Lite 00:02:30
2. Chapter 2 : Developing Our First TensorFlow Model
   • Hello World in TensorFlow 00:05:02
   • Debugging Our Model 00:05:25
   • Parameter Study 00:05:59
   • Overfitting 00:06:31
   • Deployment in iOS with TensorFlow Lite 00:15:19
3. Chapter 3 : Handwriting Recognition App
   • Introduction to the Problem and Dataset 00:04:21
   • Developing the Handwriting Recognition Model 00:04:15
   • Parameter Study 00:03:59
   • Testing the Model 00:04:34
   • Deployment in Android with TensorFlow Lite 00:08:06
4. Chapter 4 : Pattern Recognition App
   • Data Augmentation 00:04:40
   • Developing the Pattern Recognition Model 00:04:33
   • Parameter Study and Data Augmentation 00:04:18
   • Testing the Model 00:06:48
   • Deployment in Android with TensorFlow Lite 00:05:33
5. Chapter 5 : Gesture Recognition App
   • Introduction 00:06:01
   • Developing the Gesture Recognition Model 00:04:27
   • Parameter Study and Data Augmentation 00:04:45
   • Adapting and Debugging the Model 00:07:17
   • Deployment in Android with TensorFlow Lite 00:08:11
6. Chapter 6 : Voice Recognition App
   • Introduction 00:04:54
   • Developing the Voice Recognition Model 00:03:34
   • Dropout and Dataset Generation 00:05:23
   • Deployment in Android with TensorFlow Lite 00:04:50
   • Course Summary 00:04:02
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