Giới thiệu về học tăng cường sâu với TensorFlow - Học tăng cường sâu

We will learn step by step from the basic concepts of LLM (Large Language Model) to how to fine-tune the Llama 2 model, a high-performance LLM, on the dataset of your choice.

AI Agent: A total integration of the latest AI technology! Implement various AI agents and learn to build your own AI agent using LangGraph.

This course will teach you the concept of the Google Gemini model and how to use the Gemini API, and create various AI applications using Streamlit.

This is a course to learn the concepts and usage methods of the LangChain library, and to build your own ChatGPT using the LangChain library.

This course will teach you the core theories of deep learning and how to implement deep learning code using the latest TensorFlow 2.0.

This lecture covers building practical RAG (Retrieval-Augmented Generation) systems while developing an AI customer service chatbot (AICC).

A course to learn website crawling methods using ChatGPT. Get a preview of the transformed future ChatGPT will create.

This course covers work automation using MCP (Model Context Protocol) via diverse case studies: Playwright crawling, Figma design, Notion data organization, Excel data analysis.

I'm learning how to build practical AI agents using LangGraph while working on various AI agent implementation projects using LangGraph.

This course covers data analysis using ChatGPT and Pandas utilization. Get a preview of the changed future ChatGPT will create.

This is a lecture where you build your own MCP server using Cursor and experience vibe coding, which embodies the future of developers.

This is a lecture on creating an automatic stock trading bot using the IBK Investment & Securities Xing API. Create your own automatic stock trading bot that trades automatically 365 days a year.

This lecture teaches various business automation methods using ChatGPT (Excel, PPT, crawling, image processing) and Python basics for business automation. Experience in advance the changed future that ChatGPT will create.

A course to learn deep learning paper implementation skills, implementing the GPT-1 (Improving Language Understanding by Generative Pre-Training) paper from scratch with TensorFlow 2.0.

This lecture teaches how to create diverse quant investment strategies using ChatGPT. Use ChatGPT to build your own quant investment strategy.

This course teaches the process of [idea generation - data collection - verification through backtesting] for developing a Bitcoin cryptocurrency coin bot that can generate automatic profits, as well as the principles of the AIS1, AIS2 investment strategy developed by the instructor, stock selection and buying and selling points and backtesting, and the source code for bot execution.

From the basics of deep learning natural language processing to the latest models such as Transformer and BERT, learn the principles and utilization methods of deep learning natural language processing (NLP) through various examples and practical code implementations.

Learn all the steps from A to Z required to create and operate a cryptocurrency automatic trading coin bot. (Basics of using exchange APIs, deployment using AWS) You can also learn investment strategies (such as using Kimchi Premium) that can generate risk-free returns of around 10% per year by utilizing the structural characteristics of the cryptocurrency market.

This course will teach you step-by-step the principles of Amazon Web Services (AWS) cloud computing and how to use core services through various practical exercises.

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.

We will learn about Q-learning and Deep Q-learning, and have time to implement reinforcement learning in R. We will cover the entire reinforcement learning content, from Deep Q-network to Self-imitation learning and Random Network Distillation.

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.

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.

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.

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.

From the basics of machine learning & deep learning! Creating and utilizing classification/regression/clustering/artificial neural networks with Python

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.

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.

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 implement a deep learning framework directly with Excel! Let's solve supervised learning/unsupervised learning/reinforcement learning with the implemented framework!

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.

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 :)