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Intermediate

16h

Updated 3 months ago

Deep Learning for Android Apps
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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.

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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.

Traditionally, computationally intensive deep learning (DL) models aren’t deployed to mobile devices due to their limited memory...Show More

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

An understanding of Python libraries for data processing, analysis, visualization, and deep learning

TAKEAWAY SKILLS

Python 3

Deep Learning

Deep learning models in Keras

Content

83 Lessons1 Project10 Quizzes

Getting Started with Python

7 Lessons

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)

9 Lessons

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.

TensorFlow Framework

TensorFlow Core: Low-Level TF API

TF Constants and Variables

TF Ops (Part 1)

TF Ops (Part 2)

Building Graphs and Visualizing Using TensorBoard

Quiz: TF

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.

Loading and Generating a Dataset

Data Preprocessing (Part 1)

Data Preprocessing (Part 2)

Data Splits Using the Slicing API

TF’s Image Processing Module (Part 1)

TF‘s Image Processing Module (Part 2)

Data Augmentation

Quiz: Data Processing Using TF

Keras: High-Level TF API

13 Lessons

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

Keras Layers API (Part 1)

Keras Layers API (Part 2)

Keras Sequential API

Keras Functional API

Model Subclassing (Part 1)

Model Subclassing (Part 2)

Image Processing Layers

Image Augmentation Layers

DL Model Training, Testing, and Evaluation

Callbacks

Saving and Loading a TF Model

Transfer Learning for Custom Model Creation

Quiz: Keras

Diabetes Prediction Using Keras

Project

Quick Start with Android Apps

10 Lessons

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

Structure of Android Apps

App Layout

Manifest File

App Activities

Intents and Intent Filters (Part 1)

Intents and Intent Filters (Part 2)

TensorFlow (TF) Lite

9 Lessons

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

TF Lite Framework

TF Model Conversion to TF Lite (Part 1)

TF Model Conversion to TF Lite (Part 2)

Model Optimization and Quantization

TF Lite Model Maker Library

TF Lite Interpreter (Part 1)

TF Lite Interpreter (Part 2)

TF Lite Model Metadata

Quiz: TF Lite

Image Classification Apps Using TF Lite

7 Lessons

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

Inference Using the TF Lite Model

App Structure

The MainActivity Class

The ImageClassifier Class

The Horses vs. Humans Classification App

TF Lite Task Library

Image Classification App Using Task Library

Object Detection Apps Using TF Lite

7 Lessons

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

Object Detection Using TF

App Structure

TF Lite Object Detection Model with Metadata

Task Library and ObjectDetector API

Object Detection App Using the Task Library

ML Kit for Pretrained Models

Object Detection App Using the ML Kit

10.

Appendix

6 Lessons

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

Kotlin Basics

Kotlin Lambdas and Higher-Order Functions

Kotlin Keyword to Reference Single Parameter

Kotlin Classes

More Kotlin Keywords

Kotlin Generics

DL Model Using TF, Keras, and TF Lite

Project

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

Muhammad Wasim Nawaz

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