Hari Thapliyaal

Dr. Hari Thapliyal is a seasoned professional and prolific blogger with a multifaceted background that spans the realms of Data Science, Project Management, and Advait-Vedanta Philosophy. Holding a Doctorate in AI/NLP from SSBM (Geneva, Switzerland), Hari has earned Master’s degrees in Computers, Business Management, Data Science, and Economics, reflecting his dedication to continuous learning and a diverse skill set. With over three decades of experience in management and leadership, Hari has proven expertise in training, consulting, and coaching within the technology sector. His extensive 16+ years in all phases of software product development are complemented by a decade-long focus on course design, training, coaching, and consulting in Project Management. In the dynamic field of Data Science, Hari stands out with more than three years of hands-on experience in software development, training course development, training, and mentoring professionals. His areas of specialization include Data Science, AI, Computer Vision, NLP, complex machine learning algorithms, statistical modeling, pattern identification, and extraction of valuable insights. Hari’s professional journey showcases his diverse experience in planning and executing multiple types of projects. He excels in driving stakeholders to identify and resolve business problems, consistently delivering excellent results. Beyond the professional sphere, Hari finds solace in long meditation, often seeking secluded places or immersing himself in the embrace of nature.

10 minute read

Train Tensorflow Lite Models for Android

Train Tensorflow Lite Models for Android

Course Overview:

We’ll begin by exploring the basics of Machine Learning and its various types, and then dive into the world of deep learning and artificial neural networks, which will serve as the foundation for training our machine learning models for Android.

The Android-ML Fusion: After grasping the core concepts, we’ll bridge the gap between Android and Machine Learning. To do this, we’ll kickstart our journey with Python programming, a versatile language that will pave the way for our machine learning model training

Unlocking Data’s Power: To prepare and analyze our datasets effectively, we’ll dive into essential data science libraries like NumPy, Pandas, and Matplotlib. These powerful tools will equip you to harness data’s potential for accurate predictions.

Tensorflow for Mobile: Next, we’ll immerse ourselves in the world of TensorFlow, a library that not only supports model training using neural networks but also caters to mobile devices, including Android

Regression Models Training

  1. Training Your First Machine Learning Model:
    • Harness TensorFlow and Python to create a simple linear regression model
    • Convert the model into TFLite format, making it compatible with Android
    • Learn to integrate the tflite model into Android apps for Android
  2. Fuel Efficiency Prediction:
    • Apply your knowledge to a real-world problem by predicting automobile fuel efficiency
    • Seamlessly integrate the model into a Android app for an intuitive fuel efficiency prediction experience
  3. House Price Prediction in Android:
    • Master the art of training machine learning models on substantial datasets
    • Utilize the trained model within your Android app to predict house prices confidently

Computer Vision Model Training

  1. Image Classification in Android:

    • Collect and process dataset for model training
    • Train image classification models on custom datasets with Teachable Machine
    • Train image classification models on custom datasets with Transfer Learning
    • Use image classification models in Android with both images and live camera footage

  2. Object Detection in Android

    • Collect and Annotate Dataset for Object Detection Model Training
    • Train Object Detection Models
    • Use object detection models in Android with Images & Videos

The Android Advantage: By the end of this course, you’ll be equipped to:

  • Train advanced machine learning models for accurate predictions
  • Seamlessly integrate tflite models into your Android applications
  • Analyze and use existing regression & vision (ML) models effectively within the Android ecosystem

Who Should Enroll:

  • Aspiring Android developers eager to add predictive modeling to their skillset
  • Beginner Android developer with very little knowledge of mobile app development
  • Intermediate Android developer wanted to build a powerful Machine Learning-based application
  • Experienced Android developers wanted to use Machine Learning models inside their applications.

Who this course is for:

  • Beginner Android Developers who want to train ML models and build Machine Learning based Android Applications
  • Aspiring Android developers eager to add ML modeling to their skillset
  • Enthusiasts seeking to bridge the gap between Machine Learning and mobile app development.
  • Machine Learning Engineers looking to build real world applications with Machine Learning Models

Key Concepts to Learn in this course

Do you want to train different Machine Learning models and build smart Android applications then Welcome to this course.

In this course, you will learn to train powerful

  • Image Classification
  • Object Detection
  • Linear Regression

model in python from scratch. After that you will learn to

  • Use your custom trained Machine Learning Models in Android
  • Use existing tensorflow lite models in Android Apps

Regression

Regression is one of the fundamental techniques in Machine Learning which can be used for countless applications. Like you can train Machine Learning models using regression

  • to predict the price of the house
  • to predict the Fuel Efficiency of vehicles
  • to recommend drug doses for medical conditions
  • to recommend fertilizer in agriculture
  • to suggest exercises for improvement in player performance

and so on. So Inside this course, you will learn to train your custom linear regression models in Tensorflow Lite format and build smart Android Applications.

Image Classification & Applications

Image classification is the process of recognizing different entities or things in an image or video. You can recognize animals, plants, diseases, food, activities, colors, things, fictional characters, drinks, etc with image recognition.

  • In e-commerce applications image classification can be used to categorize products based on their visual features, So it is used to organize products into categories for easy browsing.
  • Image classification can be used to power visual search in mobile apps, so users can take a picture of an object and then find similar items for sale.
  • Image classification can be used in medical apps to diagnose disease based on medical images, such as X-rays or CT scans.
  • We can use image classification to build countless recognition applications for performing number of tasks, like we can train a model and build applications to recognize
    • Different Breeds of dogs
    • Different Types of plants
    • Different Species of Animals
    • Different kind of precious stones

Object detection

is a powerful computer vision technique that can accurately identify and pinpoint the location of various objects within images or videos. By recognizing objects like cars, people, and animals, this technology empowers applications such as security surveillance, autonomous vehicles, and smartphone apps that can identify objects through the camera lens.

Key Applications:

  • Autonomous Vehicles: Cars equipped with object detection can safely navigate roads, avoid collisions, and enhance driver assistance systems.
  • Surveillance Systems: Security cameras can identify individuals, track suspicious activity, and detect intrusions.
  • Retail: Stores can monitor customer behavior, manage inventory, and prevent theft.
  • Healthcare: Medical imaging systems can detect anomalies like tumors and fractures.
  • Agriculture: Farmers can monitor crops, livestock, and detect pests or diseases.
  • Manufacturing: Quality control and automation can be improved through object inspection and robotic guidance.
  • Sports Analytics: Tracking player movements and equipment can enhance performance analysis and fan experience.
  • Environmental Monitoring: Wildlife conservation and habitat protection can benefit from object detection.
  • Smart Cities: Traffic management, public space monitoring, and waste management can be optimized.

Course content

Machine Learning & Deep Learning for Android App Development

  • What is Machine Learning
  • Supervised Machine Learning
  • Regression and Classification
  • Unsupervised Machine Learning & Reinforcement Learning
  • Deep Learning and Neural Network Introduction
  • Neural Network Example
  • Working of Neural Networks for Image Classification
  • Basic Deep Learning Concepts

Python Programming Language Short Course

  • Google Colab Introduction
  • Python Introduction & data types
  • Python Numbers
  • Python Strings
  • Python Lists
  • Python dictionary & tuples
  • Python loops & conditional statements
  • File handling in Python

Data Science Libraries for Android

  • Numpy Introduction
  • Numpy Functions and Generating Random Values
  • Numpy Operators
  • Matrix Multiplications and Sorting in Numpy
  • Pandas Introduction
  • Loading CSV in pandas
  • Handling Missing values in dataset with pandas
  • Matplotlib & charts in python
  • Dealing images with Matplotlib

Tensorflow & Tensorflow Lite for Android

  • Tensorflow Introduction Variables & Constants
  • Shapes & Ranks of Tensors
  • Matrix Multiplication & Ragged Tensors
  • Tensorflow Operations
  • Generating Random Values in Tensorflow
  • Tensorflow Checkpoints
  • Tensorflow Lite Introduction & Advantages

Training a basic regression model for Android

  • Train a simple regression model for Android
  • Testing model and converting it to a tflite(Tensorflow lite) format for Android
  • Model training for Android app development overview
  • Creating a new Android Studio Project and GUI of Application
  • Adding Tensorflow Lite Library In Android & Loading Tensorflow Lite Model
  • Passing Input to Tensorflow Lite Model in Android and Getting Output

Fuel Efficiency Prediction: Training an advance regression model

  • Section Introduction
  • Data Collection: Finding Fuel Efficiency Prediction Dataset
  • Loading Dataset in Python for Model Training
  • Handling missing Values in Fuel Efficiency Prediction Dataset
  • Handling Categorical Columns in Dataset for Model Training
  • Training and testing datasets
  • Normalization Introduction
  • Dataset Normalization
  • Training Fuel Efficiency Prediction Model in Tensorflow
  • Testing Trained Model and converting it to Tensorflow Lite Model
  • Training Fuel Efficiency Prediction Model Overview

Fuel Efficiency Prediction Android Application

  • Setting up Android Application for fuel efficiency prediction
  • Loading Tensorflow Lite models & performaning normalization in Android
  • Passing input to Tensorflow Lite model in Android and getting output
  • Testing fuel efficiency prediction android application

Training a House Price Prediction Model & Building Android App

  • Section Introduction
  • Getting dataset for training house price prediction model
  • Loading dataset for training tflite model
  • Training & Evaluating house price prediction model
  • Retraining House Price Prediction Model
  • House Price Prediction Android App
  • Test the Android App

Image Classification

  • Image Classification Introduction & Applications

Data Collection - Collecting Dataset for Training Image Classification Model

  • Data Collection Introduction
  • Finding ready to use dataset for training image classification models
  • Exploring Downloaded dataset for training custom image classification models

Train Your First Custom Image Classification Model

  • Section Introduction
  • Exploring Teachable Machine and Uploading Dataset for Model Training
  • Training, Testing and Converting Model into Tensorflow Lite
  • Attaching Metadata with Trained Tensorflow Lite Models
  • Google Colab Introduction
  • Attaching Metadata and Downloading Ready to Use Model

Training Custom Image Classification Model with Transfer Learning

  • Transfer Learning Introduction
  • Google Colab Introduction
  • Installing and Importing Libraries for Model Training
  • Uploading Dataset and Connecting Google Drive
  • Dividing dataset into train test and validation parts
  • Training Custom Image Classification Model
  • Testing the model and Converting it to Tensorflow Lite Format

Image Classification: Android App Development

  • Section Introduction

Image Picker Android - Choose or Capture Images

  • Creating a new Android Studio Project and Building GUI of Android App
  • Choosing Images from Gallery in Android
  • Capturing Images using Camera in Android
  • File Provider : Share Data Between Android Apps Securely
  • Capturing Images in Android Overview

Image Classification With Images

  • Adding Tesnorflow Lite Model & Libraries in Android
  • Analyzing and loading Tesnorflow Lite Model in Android
  • Passing Input to tflite model and getting output in Android
  • Showing Results of Custom Image Classification Model on Screen in Android

Background Of Using Tensorflow Lite Models in Android

  • Loading Tensorflow Lite Model in Android
  • Passing input to the Tesnorflow Lite model and Getting output
  • How Tensorflow Lite Models return Results in Android
  • Converts Model Output into Results in Android
  • Using Transfer Learning Trained Model in Android
  • Improving GUI of Image Classification with Images Application

Display Live Camera Footage in Android with Camera2 API

  • Creating New Android Project and handling Camera Permission
  • Displaying Live Camera Footage in Android with Camera2 API
  • How we are displaying Camera in Android
  • Getting Frames of Live Camera Footage as Bitmaps in Android

Realtime Image Classification Android

  • Adding Models and Libraries in Android Studio Proect
  • Loading Tensorflow Lite Models in Android and Passing Frames of Camera
  • Showing Models results on Screen in Android
  • Using Transfer Learning Trained Model in Android
  • Setting Confidence Threshold in Android
  • Working on GUI of Realtime Image Classification Android Application

Object Detection

  • Object Detection & Applications Introduction
  • How an Object Detection Model is Trained
  • Object Detection Resources

Dataset Collection and Annotation for Object Detection Model Training

  • What is Data Collection
  • Collecting dataset for training Object Detection model for Android
  • Exploring dataset and managing it for Android object detection model training
  • What is Data Annotation
  • Exploring Data Annotation Tool and Uploading data
  • Annotating dataset for training object detection model for Android
  • Adding Annotated Images in Dataset and Correcting Classes
  • Appling Data Augmentation and Exporting Dataset
  • Checking Health of Dataset before Model Training

Training Custom Object Detection models for Android Apps

  • Training Object Detection models Section Introduction
  • Change Structure of Annotated dataset for model training
  • Google Colab Introduction
  • Model Training Notebook and Uploading Dataset
  • Importing Libraries and Loading Annotated Dataset
  • Training Custom Object Detection Model
  • Object Detection Model Evaluation Basics
  • Testing Our Custom Object Detection Model
  • Tensorflow Lite Introduction
  • Converting Object Detection Model Into Tensorflow Lite
  • Improving Object Detection Models

Object Detection with Images for Android App Development

  • Importing Starter Android Kotlin App Code for Object Detection With Images
  • Adding libraries for Performing Object Detection in Android Kotlin App
  • Loading Object Detection Models in Android Kotlin App
  • Passing Input Images to Object Detection Models and Getting Outputs in Android
  • Getting Names, Confidence and Location of Detected Object In Android
  • Drawing Rectangles Around Detected Objects In Android
  • Drawing Names Of Detected Object On Images in Android Kotlin App
  • Making Text and Rectangle Size Dynamic in Android Kotlin App
  • Handling Rotation Of Camera Images In Android Kotlin Apps

Real Time Object Detection in Android

  • Setting up the Android Studio project
  • Real Time Object Detection Android Kotlin Application Demo
  • Displaying live camera footage inside Android Kotlin App
  • Getting frames of live camera footage as bitmaps in Android Kotlin
  • Performing object detection with frames of live camera footage in Android
  • Drawing Rectangles Around Detected Objects in Realtime in Android Kotlin Apps:

Updated:

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