Deep Learning Course with Flutter & Python - Build 6 AI Apps

Use Teachable Machine by Google to build a neural network that detects yes or no in sign language from webcam data, balance the dataset, and tune epochs and learning rate.

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.

Set up a flutter ui scaffold with a padded container and column layout, presenting a teachable machine convolutional neural network and an image to detect dogs and cats.

In this Flutter and Python deep learning lecture, add two buttons to pick an image from gallery or take a photo to feed into the neural network, with state updates.

Capture a photo from your device, store it in a variable, input the image into a neural network, and train the model on teachable machines.

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 flower type detector app by customizing a neural network in Colab, using Python in your browser to build and run the model without local installations.

Set up the project by duplicating the initial app, renaming it to flower detector, importing assets, and building a gradient splash screen in Flutter to host a neural network.

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.

Finish and test the flutter and tf lite app by updating the tf lite model and labels, rebuilding, and validating five flower classes with expansion to 131 fruits and vegetables.

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.

Download the fruits 360 dataset, unzip and organize training and test folders, create a python scripts directory, and start a local TensorFlow workflow in Jupyter to build the neural network.

Set up the dataset path, image size, and batch; augment data with rotation, shifts, and flips, then create the training generator and save the labels before building the neural network.

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.

Create a new flutter project for sentiment analysis, add a splash screen dependency, configure main.dart, integrate assets and a gradient background, and prepare the home page.

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.

Add a button using a gesture detector that sends the textfield input to a sentiment analysis API and displays the result on screen, with width set via media query.

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.

Set up the home screen with a scaffold, a linear gradient background, and a centered column inside a container for text generator and image to text generator.

Develop and wire the home screen in a Flutter and Python deep learning app, including image picking, loading state management, and parsing JSON from an HTTP API to update results.

Build an API on Red Hat OpenShift that accepts an image via post and returns a JSON string with the text inside the image.

Builds an asynchronous function in Flutter to fetch a response by uploading an image via a multipart post request to an API, including mime type checks.

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.

Set up a Flutter project for a sketch-driven image generator using a generative adversarial network, add dependencies (http, file picker, path provider, cached network image), and scaffold the home screen.

Set up the home screen scaffold in a Flutter app using a stack with a gradient container and a centered 256x256 drawing canvas, controlled by a loading flag.

Build a drawing canvas in Flutter by creating a drawing area and a custom painter that renders a list of points as lines to enable sketching.

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.

learn how to build a basic flask server to deploy a neural network, including a hello world route and a rest api for flutter applications.

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.