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Deep Learning for Android Apps

Name: Deep Learning for Android Apps
Price: 59 USD
Author: Muhammad Wasim Nawaz

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.

5.0

83 lessons

4 projects

16h

Created by Muhammad Wasim Nawaz

What's included

83 lessons across 10 modules
10 quizzes with instant feedback
4 projects in preconfigured environments
AI Tutor on every lesson

Join 3.1 million developers at

What You'll Learn

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

83 Lessons1 Project10 Quizzes

Getting Started with Python

Learn how to use Python's core and collection data types, control flow, functions, file handling, and key libraries.

Python Data Types

Python Collection Data Types

Conditional Statements and Loops

Functions in Python

File Handling in Python

Python Libraries and Frameworks

Quiz: Python

Machine Learning (ML) and Deep Learning (DL)

Get started with machine learning principles, supervised/unsupervised learning, neural networks, and transfer learning for Android apps.

What is Machine Learning?

Supervised Learning: Regression

Supervised Learning: Classification

Unsupervised Learning

Neural Networks and Deep Learning

Convolutional Neural Networks

Recurrent Neural Networks

Transfer Learning

Quiz: ML and DL

Customer Segmentation with K-Means Clustering

Project

TensorFlow (TF)

7 Lessons

Examine TensorFlow's framework, core APIs, constants, operations, graph building, and visualizing with TensorBoard.

Cats vs Dogs Classification with Convolutional Neural Networks

Project

Dataset Processing Using TensorFlow

8 Lessons

Break down complex ideas for efficient dataset handling using TensorFlow's tf.data API.

Keras: High-Level TF API

13 Lessons

Take a closer look at Keras APIs, model creation, layers management, training, and evaluation.

Diabetes Prediction Using Keras

Project

Quick Start with Android Apps

10 Lessons

Follow the process of building, structuring, and managing components in Android apps.

TensorFlow (TF) Lite

9 Lessons

Master deploying optimized deep learning models on mobile with TensorFlow Lite.

Image Classification Apps Using TF Lite

7 Lessons

Step through creating image classification apps using TF Lite models and Task Library on Android.

Object Detection Apps Using TF Lite

7 Lessons

Walk through building Android object detection apps using TF Lite and ML Kit.

10.

Appendix

6 Lessons

Explore Kotlin essentials, from basics to advanced concepts like classes, lambdas, and generics.

DL Model Using TF, Keras, and TF Lite

Project

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

About this course

WHY I WROTE 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.

Learn more about Muhammad

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