強化学習入門からDeep Q-learning/Policy Gradientまで

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

Do you want to take your first step into the world of data? Learn machine learning and deep learning, the core technologies of AI, with Python. This course will guide you step by step, from the basics of machine learning and deep learning to practical applications. Traditional machine learning and deep learning are based on many of the same principles and technical systems. Therefore, this course does not separate the two into separate subjects, but rather organizes them into one connected course so that beginners can increase their understanding of machine learning as a whole.

Understand the operating principles of deep learning-based generative AI models and acquire utilization methods through hands-on practice.

You can acquire an overview of machine learning and deep learning, how to use basic tools, and the Python language knowledge necessary for learning in a short period of time.

Financial data such as stocks, exchange rates, coins, real estate, and bonds are easily accessible and provide us with opportunities to make money, but most people miss valuable opportunities because they do not know their characteristics and how to handle them. Through this lecture, you can learn how to understand the meaning of various financial data and how to analyze them using Python.

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!

Navigate the data ocean, explore AI's future. This course explores the vast tech currents of Big Data and AI, examining their innovation in our lives and work.

Learn how to build deep learning models directly through intuitive and Pythonic PyTorch. This reflects the latest PyTorch version.

This course teaches you how to use generative AI models using the OpenAI API. In this course, you will learn the basics of using the API, how to use various language models, and practical programming examples and applications to learn how to apply generative AI in real life.

Are you new to coding or looking to take your coding skills to the next level? This course will introduce you to Python, a powerful and flexible language, designed for all levels of learners. Taught in a “follow-along” fashion, this course will allow you to learn by actually writing and running code. You’ll discover why Python is one of the most popular programming languages. From artificial intelligence to web development, learning the Python language offers endless possibilities, and it’s an opportunity to greatly expand your personal potential.

JavaScript, which is known to all web developers, now explore the world of machine learning with this powerful language! This course learns how to build and deploy machine learning models using JavaScript and the powerful machine learning library Tensorflow.js. It guides you step-by-step through all the technologies required to develop web-based machine learning applications. Through this course, learners will systematically understand the core principles of machine learning. In addition, you will learn how to develop deep learning models using JavaScript and Tensorflow.js APIs, how to utilize transfer learning based on pre-trained models, and how to apply all this knowledge interactively in a browser environment.

This course covers everything from the basic concepts of recommendation systems to the principles of applying deep learning. Learn about various recommendation algorithms such as collaborative filtering, content-based filtering, and hybrid recommendation systems, and develop practical skills for developing recommendation services!

In the modern business world, data security and fraud prevention have become more important than ever. This lecture focuses on how to effectively detect and analyze fraudulent transactions using artificial intelligence (AI). Using Python and machine learning, we will build an AI model that can detect and detect anomalies in various industries, such as fraudulent transactions, credit card fraud, and production line abnormalities, early.

Natural Language Processing (NLP) is one of the fastest growing areas of artificial intelligence. This course covers a wide range of topics, from the basics of NLP to the latest NLP techniques using deep learning. In particular, it provides an in-depth understanding of cutting-edge generative models such as ChatGPT.

Understand the basic concepts and latest trends of big data, and through technical understanding of big data analysis techniques, extract data patterns to present methods for business application. Through big data, you can create new businesses and develop insights into future business environments.

By understanding the working principles of various recommendation algorithms, you can create your own personalized recommendation algorithm!

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.

To keep up with recent deep learning trends, we examine the context of deep learning's groundbreaking advancements.

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.

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!

Through this course, you will learn and use various functions of machine learning agents, such as multi-agents, curriculum learning, and distributed learning. You will also learn about reinforcement learning algorithms that can respond to curiosity-based exploration and variable inputs.

Through this course, students will learn various reinforcement learning theories and implement them themselves, as well as create a reinforcement learning environment to test the reinforcement learning algorithm implemented using Unity Machine Learning Agents.

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!

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 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.

Make your own AI autonomous vehicle! This is a course where you can learn about AI and reinforcement learning in the most fun and fastest way. This course was produced by the developer ranked #1 in DeepRacer Korea.

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.

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.

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 :)