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Deep Learning for Android Apps
Delve into deploying DL models on Android using TensorFlow Lite. Gain insights into training, converting models, and practical applications through case studies for efficient mobile integration.
83 Lessons
4 Projects
16h
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- An understanding of Python libraries for data processing, analysis, visualization, and deep learning
- The ability to apply TensorFlow and Keras to train deep learning models
- Working knowledge of training and evaluating deep learning models on various datasets
- The ability to convert TensorFlow models to the TensorFlow Lite format and deploy them to Android applications
Learning Roadmap
1.
Getting Started with Python
Getting Started with Python
Learn how to use Python's core and collection data types, control flow, functions, file handling, and key libraries.
2.
Machine Learning (ML) and Deep Learning (DL)
Machine Learning (ML) and Deep Learning (DL)
Get started with machine learning principles, supervised/unsupervised learning, neural networks, and transfer learning for Android apps.
3.
TensorFlow (TF)
TensorFlow (TF)
7 Lessons
7 Lessons
Examine TensorFlow's framework, core APIs, constants, operations, graph building, and visualizing with TensorBoard.
4.
Dataset Processing Using TensorFlow
Dataset Processing Using TensorFlow
8 Lessons
8 Lessons
Break down complex ideas for efficient dataset handling using TensorFlow's tf.data API.
5.
Keras: High-Level TF API
Keras: High-Level TF API
13 Lessons
13 Lessons
Take a closer look at Keras APIs, model creation, layers management, training, and evaluation.
6.
Quick Start with Android Apps
Quick Start with Android Apps
10 Lessons
10 Lessons
Follow the process of building, structuring, and managing components in Android apps.
7.
TensorFlow (TF) Lite
TensorFlow (TF) Lite
9 Lessons
9 Lessons
Master deploying optimized deep learning models on mobile with TensorFlow Lite.
8.
Image Classification Apps Using TF Lite
Image Classification Apps Using TF Lite
7 Lessons
7 Lessons
Step through creating image classification apps using TF Lite models and Task Library on Android.
9.
Object Detection Apps Using TF Lite
Object Detection Apps Using TF Lite
7 Lessons
7 Lessons
Walk through building Android object detection apps using TF Lite and ML Kit.
10.
Appendix
Appendix
6 Lessons
6 Lessons
Explore Kotlin essentials, from basics to advanced concepts like classes, lambdas, and generics.
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
Traditionally, computationally intensive deep learning (DL) models aren’t deployed to mobile devices due to their limited memory and computational power. But with a lightweight framework such as TensorFlow (TF) Lite, DL models can be deployed and run efficiently on mobile and edge devices, benefiting from fast response time, data privacy, and cost reduction in cloud computing resources.
This course aims to equip you with the techniques to deploy DL models on Android devices using the TF Lite framework. You’ll start with a quick introduction to Python and machine learning. Next, you’ll explore different learning paradigms and DL models. You’ll also get practical knowledge of the applications of TF in the context of DL. The course covers TF Lite for mobile applications with different case studies showcasing its use in Android apps.
By the end of this course, you’ll gain the necessary skills to train DL models, convert them to the TF Lite format, and deploy them into your Android apps.
ABOUT THE AUTHOR
Muhammad Wasim Nawaz
Dr. Muhammad Wasim Nawaz, an assistant professor of Computer Engineering, has teaching and research experience in Deep Learning, Machine Learning, Computer Vision, Data Science and Digital Signal & Image Processing.
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