Mobile Machine Learning for Android using Tensorflow lite
Mobile Machine Learning for Android using Tensorflow lite
Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 44100 Hz, 2ch | Size: 1.92 GB
Genre: eLearning Video | Duration: 66 lectures (3 hour, 42 mins) | Language: English
Chose your datasets, train Machine Learning models and develop smart Android Applications, Smart Android App development.
What you'll learn
Training models on datasets and developing Android Applications
Complete understanding of Machine Learning ,Deep Learning and Neural Networks
Learn Basics of Python Programming language
Learn popular Machine Learning libraries like Numpy,Pandas and MatDescriptionlib
Learn basics of Tensorflow 2.0
Learn about Tensorflow Lite
Generating Tensorflow lite model from Keras model, saved model, concrete function
Train and deploy classification and regression models
Training recognition models and creating Android Applications for those models
Requirements
Basic knowledge of Android Development
Basic Programming skills
Description
Tired of traditional Android Development courses? Now its time to learn something new and trending for Android. Machine Learning is at its peak and Android App Development is also in demand than what is better than learning both?
This course is designed for Android developers who want to learn Machine Learning and deploy machine learning models in their android apps using TensorFlow Lite. This course will get you started in building your FIRST deep learning model and android application using deep learning. We will learn about machine learning and deep learning and then train your first model and deploy it in android application using TensorFlow lite . All the materials for this course are FREE.
Course includes examples from basic to advance
A very simple example
Example using saved model
Example using concrete function
Predicting fuel efficiency of automobiles (Regression Example)
Predicting Fitness of a person (Classification example)
Recognizing handwritten digits
Flowers Recognition Example
Stones Recognition Example
Fruits Recognition Example
We will start by learning about the basics of Python programming language. Then we will learn about some famous Machine Learning libraries like Numpy, MatDescriptionlib, and Pandas. After that, we will learn about Machine learning and its types. Then we look at Supervised learning in detail. We will try to understand classification and regression through examples. After we will start Deep learning. We start by looking and the basic structure of neural networks. Then we will understand the working of neural networks through an example.
Then we will learn about Tensorflow library and how we can use it to train Machine Learning models. After that, we will look at how we can convert our model to tflite format which will be used in Android Application. There are three ways through which you can get a tflite file
From Keras Model
From Concrete Function
From Saved Model
We will cover all these three methods in this course.
We will learn about Feed Forwarding, Back Propagation, and activation functions through a practical example. We also look at cost function, optimizer, learning rate, Overfitting, and Dropout. We will also learn about data preprocessing techniques like One hot encoding and Data normalization.
Next, we implement a neural network using Google's new TensorFlow library.
You should take this course If you are an Android Developer and want to learn the basics of machine learning(Deep Learning) and deploy ML models in your Android applications using Tensorflow lite
This course provides you with many practical examples so that you can really see how you can train and deploy machine learning model in android. We will use Android Studio for developing Android Application for models we trained.
Another section at the end of the course shows you how you can use datasets available in different formats for a number of practical purposes.
After getting your feet wet with the fundamentals, I provide a brief overview of how you can add your machine learning model in google existing android machine learning projects templates.
Suggested Prerequisites:
Basic Knowledge of Android Development
TIPS (for getting through the course):
Write code yourself, don't just sit there and look at my code.
Who this course is for:
Beginner Android Developers want to make their applications smart
Android Developers want to use Machine Learning in their Android Applications
Developers interested in the practical implementation of Machine Learning and computer vision
Students interested in machine learning - you'll get all the tidbits you need to add machine learning models in android
Professionals who want to use machine learning models in Android Application.
Machine Learning experts want to deploy their models in Android
Who this course is for:
Android Developers curious about Machine Learning
People having basic knowledge of Android Development
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