
Discover how to install flutter on Windows and Mac. Download the flutter SDK, add it to your path via environment variables, and run flutter doctor -v to verify dependencies.
Develop a cat-vs-dog detector using a convolutional neural network built with Teachable Machine, featuring drag-and-drop data and one-click training for rapid AI prototyping.
Configure a Flutter project by cleaning default files, creating an assets folder and pubspec.yaml entries, then implement a splash screen with a title, image, and navigation to home page.
Fix the min sdk error by updating android/app/build.gradle to minSdkVersion 19, add the flight package and image_picker dependency, and begin implementing flight functions in home.dart.
Load the model in a Flutter app, then classify images using a tf lite model and update a loading state while preventing memory leaks with proper disposal.
Pass an image from the gallery to the neural network, call the classify image function, manage loading state, and display the predicted outputs in the app UI.
Develop a responsive Flutter home screen with a styled take a photo button, using media query, gesture detector, and padding; next, build neural networks in Google Colab.
Learn how to use Google Colab to build neural networks, run Python code, install packages with pip, set GPU runtime, import files, and work with Jupyter notebooks.
Copy and paste the previous app to create a new neural network project named Ifrit. Then update colors, texts, and assets in Flutter to match the new network.
Set up the home screen by updating titles, assets, and a blue gradient theme, run the app, and prepare a 131 fruit and vegetable classifier.
Explore sentiment analysis by sending phrases to an API and retrieving emotion predictions, illustrated with a demo that shows joy and discusses applications like stock market insights from tweets.
Build the home screen with a loading boolean and a text field connected to a text editing controller. Send input to an API for sentiment analysis and display the results.
Set up an api service in a flutter app using rapid api’s text analysis, obtain your api key, configure base url and headers, and post requests with json parsing.
Build an API on Red Hat OpenShift that accepts an image via post and returns a JSON string with the text inside the image.
Set up live camera functionality in flutter by adding the camera dependency, detecting cameras, initializing a back camera with medium resolution, and capturing photos every five seconds for http requests.
Finalize the live camera feature in a Flutter app with a scaffolded gradient UI, a live preview, periodic predictions, and navigation to the live captions screen.
Add drawing with a gesture detector tracking onPanDown, onPanUpdate, onPanEnd, updating points. Render the sketch with a custom painter using black stroke and round caps.
Get the dataset here:
https://www.dropbox.com/sh/li7f5516vthdkww/AABh6SegmZPuqtbiQot0T8qga?dl=0
Get the GAN Python Notebook here:
https://www.dropbox.com/s/284ttsdsez96404/sketch2real.ipynb?dl=0
Get the dataset here:
https://www.dropbox.com/sh/li7f5516vthdkww/AABh6SegmZPuqtbiQot0T8qga?dl=0
Get the GAN Python Notebook here:
https://www.dropbox.com/s/284ttsdsez96404/sketch2real.ipynb?dl=0\
Build a Flask server that loads a saved neural network and preprocesses images, exposing generate and download routes to predict and serve outputs for a Flutter app.
https://www.dropbox.com/s/vh09auco5w8xlqr/DeepLearningFlutter.zip?dl=0
Join the most comprehensive Flutter & Deep Learning course on Udemy and learn how to build amazing state-of-the-art Deep Learning applications!
Do you want to learn about State-of-the-art Deep Learning algorithms and how to apply them to IOS/Android apps? Then this course is exactly for you! You will learn how to apply various State-of-the-art Deep Learning algorithms such as GAN's, CNN's, & Natural Language Processing. In this course, we will build 6 Deep Learning apps that will demonstrate the tools and skills used in order to build scalable, State-of-the-Art Deep Learning Flutter applications!
After completing this course you will be able to:
Learn the tools and skills to develop any AI idea you have onto a mobile phone.
Have a clear understanding of different types of Neural Networks and how you can use them to your advantage.
Build any Neural Networks in Python and then export them into your Flutter application.
Use different strategies to get neural networks with 90%+ accuracy.
Why should you choose this course?
This course covers all the steps that one should take to create a Flutter application that uses State-of-the-Art Deep Learning approaches.
It is extremely hard to find good tutorials that teach you how to implement these algorithms, so the instructor, Samuel Boylan-Sajous, who has been programming for years, and has a Computer Science degree at the University of Guelph, has decided to share all of his knowledge with other students that are passionate about Deep Learning and building practical A.I. applications.
Here's what's included in the course:
Building a neural network on Teachable Machine and exporting it into Flutter project
Building a neural network in Keras/Python on Google Collab/Jupyter Notebook and exporting it into Flutter project
Using API's to perform some Natural Language Processing and implementing those algorithms into our app.
Build a GAN in python and implement it into your flutter application.
And much more!
This course is for you if ...
You're interested in building real native mobile apps for the two most popular mobile platforms - iOS and Android
You're passionate about Deep Learning/AI or Mobile Development and want to expand your knowledge.
You want to expand your Deep Learning/AI portfolio with 6 Cutting Edge Deep Learning apps!
Want to learn State-of-the-Art Deep Learning algorithms and how you can use them on a mobile device!
Course prerequisites:
Knowing mobile development will help a lot but is not a hard requirement (You would be fine if you only knew CSS/HTML & Object-Oriented Programming, just do some research on what Flutter is)
Know some theory behind Neural Networks (Know how data is processed in a Neural Network)
Our Promise
Teaching our students is my job and I am committed to it. If you have any questions about the course content or anything related to any topic, you can always post a question in the course or send me a direct message.