[TensorFlow 2] Chinh phục hoàn toàn deep learning Python - Các kỹ thuật mới nhất của GAN, BERT, RNN, CNN

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.

You will learn how to process images and videos with OpenCV, and how to identify and recognize faces and various objects using deep learning. This is a course where you will learn fun projects such as drowsiness prevention, age and gender identification, counting the number of people passing by, face tracking, and YOLO that finds bad apples and tangerines, along with theories.

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 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 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 course teaches you how to implement deep learning papers by implementing the YOLO (You Only Look Once) paper from scratch using TensorFlow 2.0.

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.

Time is constantly flowing, and data is constantly accumulating. In this dynamic world, time series data plays a vital role in every aspect of our lives. From the volatility of financial markets to subtle signals of climate change, time series data captures it all. Now learn how to interpret and leverage this powerful data using the power of Python and deep learning!

Analyze over 40 types of economic indicators and stock data with AI to create your own powerful stock price analysis model that predicts not only S&P 500 and QQQ ETF, but also individual stocks!

This course covers everything from the basics to hands-on practice of AI platforms, and is a program where you can learn AI image generation technology. Learners can understand the fundamental concepts of AI and develop the ability to generate AI images using various tools and technologies. Through this course, you can enhance your understanding of the AI field and learn how to directly generate AI images in real-world applications.

This course starts with the question "If I had been on the Titanic, could I have survived?" and completes a full-stack project that develops an AI model to predict survival probability based on actual data and serves it as a web service. You'll practice the entire process of AI and web development, from deep learning modeling using PyTorch, building a backend server with FastAPI, to implementing a user interface with Next.js.

From core theories of deep learning and CNN to various CNN model implementation methods, and practical deep learning development know-how through real-world problems, If you want to become a deep learning CNN technology expert based on Pytorch, join us in this lecture :)

You can create a deep learning analysis environment in the cloud using Docker. When Docker images managed by Google, MS, etc. are combined with the cloud, you can work with the latest deep learning analysis methods on your computer.

The PyTorch_Project Edition, understood instantly with code, implements practical deep learning projects using image, video, and NLP data, understanding model structure and flow through code-centric learning over theory.

This course is an introductory program designed so that learners who are new to AI technology can easily understand and follow along. Through the lectures, you will learn the basic concepts and usage methods of AI platforms, and conduct hands-on practice directly generating images using various AI tools. Upon completing this course, you will become much closer to AI and acquire the capability to create AI images that can visually implement creative ideas.

RAG (Retrieval-Augmented Generation) from theory to the latest multimodal and agent-based RAG! This is a hands-on lecture designed to be understandable even for non-majors. From paper reviews to practical code implementation, it's designed so that even those encountering RAG for the first time can easily follow along.