English | Size: 4.69 GB
Genre: eLearning
What you’ll learn
Flutter Deep Learning
Flutter Machine Learning
Flutter Artificial Intelligence
Skills and Techniques to develop any Artificial Intelligence idea into a mobile phone app
Implementing (NLP) Natural Language Processing Algorithm for Mobile Apps Development
Implementing (CNN) Convolutional Neural Network for Mobile Apps Development
Optical Character Recognition
Understanding of Different Types of Neural Networks & How you can use them
you will learn and make 15+ Ai Apps
and Much more.
In this course you will learn how to make your own Artificial Intelligence Apps using Flutter (Android+iOS) with TensorFlow Lite.
We will develop 15+ AI Apps with Flutter using TensorFlow Machine Learning and Deep Learning Concepts. In this course you will also learn how to train a model/machine for your apps. And how to import and use these trained models after training in your flutter app (android+iOS app).
This is a complete step by step course. At the end of this course you will be able to make your own Ai, Deep Learning and Machine Learning Apps for the Android Smart Phones and iOS [iPhones] using Flutter SDK with TensorFlow Lite.
TensorFlow Lite is an open source deep learning framework for on-device inference. See the guide. Guides explain the concepts and components of TensorFlow Lite.
TensorFlow Lite is a set of tools to help developers run TensorFlow models on mobile, embedded, and IoT devices. It enables on-device machine learning inference with low latency and a small binary size.
Among these 15+ Apps we will also develop 3 additional apps using Firebase Machine Learning Kit which is also known as Firebase ML Kit.
Deep learning is an AI function that mimics the workings of the human brain in processing data for use in detecting objects, recognizing speech, translating languages, and making decisions. Deep learning AI is able to learn without human supervision, drawing from data that is both unstructured and unlabeled.
Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
Who this course is for:
Anyone can join this course.
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