[OpenCV] Pythonディープラーニング映像処理プロジェクト2 - 不良リンゴを見つけよう！

This is a machine learning and deep learning project preparation course that uses Python to process Boston Marathon big data into the desired format and uses various charts and technologies to create valuable information.

In this course, you will learn the basics of Raspberry Pi and Python and practice creating simple but essential self-driving car and remote home monitoring projects.

In this course, beginners will learn the basics of Python and develop practical application skills while following interesting projects. - Vending machine program - Missile trajectory tracking program - Introduction to game program PyGame - Mashup service for nearby searches

This is a practical project lecture that collects data and analyzes big data using Arduino and wind sensors.

Easy, Usable Computer Vision Projects: Step-by-step learning of practical CV projects. Boost skill with real examples.

This course aims to develop practical Python programming skills for real-world projects such as deep learning and the Internet of Things. In order to save your time, we have minimized installation and included everything in one course so that you can learn the key content repeatedly.

There are many projects that want to provide deep learning programs made with Python as web or mobile services. Learn the process of making Python-based deep learning codes such as TensorFlow, PyTorch, YOLO, OpenCV, and Keras into web service APIs and provide them as mobile web apps, and apply them in practice.

Digital marketing that makes money, such as Naver Shopping and Coupang Partners, requires consistency, but you can approach it easily and effectively with scientific analysis and automation. We will teach you the key secrets of creating a program that collects and analyzes popular products, keywords, blogs, and shopping information.

This is a course that teaches you practical examples that can be used in projects using Cloud Functions, Google's representative Serverless BaaS (Backend as a Service) solution that can be used without a server.

AI Artificial Intelligence, it goes without saying that mobile is the trend. But how do you learn machine learning and deep learning and use them on mobile? Now, let's learn useful mobile Computer Vision projects that are used in daily life.

It goes without saying that AI artificial intelligence is the trend. But what can you use machine learning and deep learning for? Now, let's learn useful Internet of Things IoT Computer Vision projects that are used in daily life.

This course is a fun project that learns theories and processes images and videos with OpenCV (Open Source Computer Vision Library) and uses deep learning modules (dnn, Deep Neural Network) to identify and recognize faces, eyes, and various objects.

This is a comprehensive deep learning project course that teaches the latest deep learning techniques, GAN, BERT, RNN, and CNN, while creating various useful projects based on Python and TensorFlow 2 along with theories.

This course is based on Ionic and focuses on a simple but useful petlist project. It will help you develop practical mobile web app development skills in a short period of time.

This is a practical project capacity building course that follows the Pet Store shopping mall Blueprint Application that can be applied to various fields using Angular Front-end and Firebase Back-end.

This is a comprehensive course on Keras theory and practice that uses Keras and Python, the common language of artificial intelligence, to learn various useful machine learning regression, classification, and deep learning neural network projects along with theory.

This is a comprehensive machine learning project course that learns various useful machine learning regression and classification projects along with theory using Python and TensorFlow 2 based on Boston Marathon big data.

In this course, you will understand the concepts of data visualization and learn the 5 core uses of charts and 10 core charts so that you can appropriately utilize them in your data analysis projects.

This course aims to develop practical Python programming skills for real-world projects such as deep learning and the Internet of Things. It is designed to save time for busy people and to help them learn key content repeatedly, all in 100 minutes.

This course is based on Vue and condenses the simple but useful petlist project into a 100-minute core lecture, allowing you to develop mobile web app development skills that can be applied in real life in a short period of time.

This course is a fun project that learns theories and processes images and videos with OpenCV (Open Source Computer Vision Library) and uses deep learning modules (dnn, Deep Neural Network) to identify and recognize faces, eyes, and various objects.

This course teaches you how to perform Object Detection using the latest high-performance deep learning model using the TensorFlow Object Detection API through simple Colab exercises, and learns the principles of the latest deep learning Object Detection models.

This is a comprehensive machine learning project course that learns various useful machine learning regression and classification projects along with theory using Python and TensorFlow 2 based on Boston Marathon big data.

Let's create a web app that uploads an image and uses OpenCV to process it and detect faces.

AI Artificial Intelligence, it goes without saying that mobile is the trend. But how do you learn machine learning and deep learning and use them on mobile? Now, let's learn useful mobile Computer Vision projects that are used in daily life.

This is a lecture to build an AI portfolio by doing a clone project of airbnb data science team's Amenity Detection project, an excellent TensorFlow utilization case, and organizing the experience on a GitHub blog.

This newly designed course, drawing from the instructor's initial deep learning failures, helps you progressively learn deep learning essentials: math, theory, PyTorch-based implementation, transfer learning, and GPT's core Transformer.

If you want to properly learn OCR technology that's truly used in practice, this one course is all you need! Aiming for over 98% accuracy even with unstructured documents and complex layouts, based on the latest SOTA models and real-world know-how, we build enterprise-level OCR projects together.

This course is a step-by-step learning process that covers the principles of image and object recognition using deep learning, from fundamentals to the latest models. - Building Foundations: Understanding tensors and basic neural network structures with PyTorch - Understanding Images: Learning computer vision concepts, image data structures, and augmentation techniques - CNN Model Training: Hands-on image classification practice with Convolutional Neural Networks (CNN) using datasets like CIFAR-10 - Transfer Learning: Leveraging pre-trained models to achieve fast learning with limited data - Object Detection: Understanding and practicing with the latest object detection models including R-CNN, YOLO, SSD, DETR - Segmentation: Experiencing pixel-level object segmentation through U-Net and Mask R-CNN

When researching AI or conducting a project using it, basic paper implementation is essential. Let's upgrade our practical skills by implementing an actual paper through this lecture!