Hands-on AI — the book's order

This is an app that uses CNN to learn QuickDraw's hand drawing data and recognize the learned drawings. This app can recognize 100 drawings.

MediaPipe's facial recognition solution quickly detects the presence or absence of a face and displays six key parts. The functionality is simple and the processing speed is very fast.

MediaPipe provides a facial recognition solution. It estimates 468 points on the face and converts them into a 3D shape to fit the face.

MediaPipe provides a pose recognition solution. It estimates the positions of 33 major parts of the entire human body and connects them to display them like a skeleton.

MediaPipe provides a solution for hand and finger tracking. It can track the movements of 21 key parts in 1-4 hands.

Integrates MediaPipe's hand, face net, and pose features.

You can create character animations using MediaPipe and TensorFlow. This feature recognizes a person's face or the entire person and makes the character move along.

This is an app that uses MediaPipe's FaceMesh to recognize faces and create virtual avatars based on them.

This is a face recognition app created using TensorFlow. This app guesses and displays information such as expression and age from the recognized face.

This application can recognize objects commonly seen in everyday life in 3D. It estimates and displays the pose of objects learned with specific data in real time.

This is a game that removes frost from images captured by a camera. The game was created using a hand recognition model.

This is a game where you find objects around you that match the presented emoticon. Images are predicted through CNN, and the prediction matches the correct answer (emoticon).

This is an app that recognizes people's poses and faces in real time from captured videos and turns them into moving character animations.

It is a web-based machine learning learning tool designed to allow anyone to quickly and easily create machine learning models. You can create image, audio, and pose models.

This is an application that adds a machine learning library to Scratch 3. Machine learning functions are organized in the form of blocks, so they can be used similarly to existing Scratch blocks.

You can play the 2048 game with image recognition using CNN.

This is a Pac-Man game that allows you to manipulate direction through image recognition. A pre-trained MobileNet model was used to recognize motion in four directions.

A DQN-based self-driving car simulator. This simulator leverages DQN to help cars learn how to drive themselves.

This is an app that demonstrates the process of learning how to avoid a ball using DQN. The agent receives information from sensors and receives more rewards the longer it stays alive.

This is an app that demonstrates the learning process of avoiding enemy planes using DQN. The agent receives information from sensors and the longer it stays alive, the more rewards it receives.

You can see the process of learning the snake game using DQN. Reinforcement learning was carried out so that you receive a negative reward when you hit a wall or your own body, and you receive a positive reward when you eat fruit.

You can see the Flappy Bird game learning process using neural networks and genetic algorithms. As the birds pass through generations, they become better at flying based on information from previous generations.

We used a supervised learning algorithm to teach Gorilla how to play the game. We used a simple neural network built with TensorFlow.

You can see the process of learning the 2048 game using the MCTS algorithm. If you provide a large number of simulations in one move, you can reach 2048 with a high probability.

Keras is a deep learning library that helps you quickly and easily implement artificial intelligence. In the demo, you can check out Keras' learning process and performance in your browser.

ConvNetJS is a library that allows you to train deep learning models in the browser. Through the demo, you can visualize the learning process and results of the neural network.

This is a game that learns from QuickDraw's hand drawing dataset to recognize what the user's drawing is. The game uses CNN to learn and recognize drawings.

Using CNN, we created an artificial intelligence that learns the game of Tetris. It is repeatedly trained to place blocks similar to the match data of champions.

This is a program implemented to help users easily understand the inner workings of CNN. You can visualize the image recognition process step by step.

Load deep learning models pre-trained with TensorFlow and output them as 3D visualization scenes. You can visualize the processing of each model.

This is a drawing app that predicts what the user has drawn and presents icons with a high probability of matching so that the user can select them.

This is a game where the user draws a picture presented as a problem and the learned artificial intelligence guesses what the picture is.

This is a drum composition app created using a pre-trained drum RNN model. If you provide a seed pattern, it continuously generates the next pattern based on it.

Using the Performance RNN model, a song is created by referring to the frequency of use of key signatures. The model learned musical structures and patterns using LSTM.

Using LSTM, it remembers the strokes of letters written by the user and displays predicted letters based on them.

This is a continuous drawing app created using RNN. It learns the QuickDraw data set and has a function to continue drawing like a hand drawing when the user draws.

This is an app that utilizes the MusicVAE model to generate repeating melodies. MusicVAE can create harmonious melodies by smoothly connecting multiple melodies.

We created an arpeggio pattern to play using Magenta and Improv RNN. Improv RNN conditionally generates a melody based on the basic chord progression.

This is a game where you can control your character through motion recognition using TensorFlow and a camera. The game character follows the user's actions and attacks the opponent.

This is a program that allows you to examine the learning process of GAN in real time with an interactive visualization tool. The operation process of the generator and discriminator for 2D data is presented as an example.

This is a GAN model that creates a face image with selected attribute values. You can create the desired character image by changing the model type and attribute values.

This is a service that uses the Pix2Pix GAN model to receive input source images such as sketches and convert them into other types of images.

This is a service that allows you to animate landscape photos using the AnimeGAN model.

This is an autonomous driving demo visualized using the KITTY and NuScenes datasets using streetscape.gl (a tool for visualizing autonomous driving data).

An autonomous driving simulator built with WebGL and Three.js. The simulator is designed to avoid static and dynamic obstacles and reach the destination.

You can train a self-driving car by applying various reinforcement learning algorithms. You can check each reinforcement learning process and its results.

This is an app that shows ethical issues that can arise during autonomous driving in the form of a game. There is no correct answer, and you can check other people's choices.

A Korean AI portal service where you can select and use famous latest AI models from around the world, such as GPT-5 and Claude 3.5, for free in one place.

OpenAI's flagship conversational AI. Powered by the latest GPT-5 model, it offers multimodal capabilities to chat with voice, analyze images, and draw pictures, in addition to text.

Microsoft's AI assistant based on GPT-5 (formerly Bing Chat). Combined with the Bing search engine, it answers based on the latest information and draws pictures via DALL·E 3.

Gemini (formerly Bard) is Google's latest multimodal AI model. It can understand and process text, code, and images, and provides a variety of information in conjunction with Google services.

An AI image generation tool that can be used for free without logging in. Formerly famous as 'DALL·E mini'.

Microsoft's image creation tool powered by DALL·E 3 (formerly Bing Image Creator). It converts sentences entered by the user into high-quality images.