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 For More Courses Visit & Bookmark Your Preferred Language Blog Buy Premium From My Links To Get Resumable Support,Max Speed & Support Me Download now LINK Download now LINK Download now LINK Download now LINK

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