Introduction to Deep Learning

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 guides readers to deep learning with the most appropriate pace and straight direction, one step from concept to formula and one step from coding. In addition, there are over 100 graphs, illustrations, and diagrams, so you can easily and quickly accept abstract concepts. Another unique feature of this lecture is that you can start practicing right away by simply accessing a web browser without installing a program. After comfortably understanding the theory, you can directly code and conquer four representative deep learning problems with your eyes, so it is not lacking as a textbook for deep learning. The concepts or terms that you must go over are reviewed twice in the ‘Wait! Let’s move on to the next’ corner in the middle of the text and the ‘Memory Card’ corner at the end of the chapter to increase the learning effect. Let’s quickly overcome deep learning head-on with ‘Do it! Introduction to Deep Learning.’

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

👉 This course is ideal for those who want to properly understand how AI changes and its limitations. Is AI finished once it’s created? This course explains how AI changes through learning and why the results can differ even with the same AI. It clearly explains the meaning of the learning process and the technical reasons why performance may improve or decline.

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!

Have you been getting stuck on linear algebra formulas every time you try to study AI? Instead of memorizing formulas, this lecture shows you visually how vectors and matrices actually move in space. By the end of the course, you will be able to interpret the mathematics behind neural networks, PCA, and SVD on your own.

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 :)

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.

You will learn about Transformers inside out by directly implementing everything from Multi-Head Attention to the Original Transformer model, BERT, the Encoder-Decoder-based MarianMT translation model, and the Vision Transformer in code.

Starting with the question, "If I were on the Titanic, would I have survived?", this course completes a full-stack project where you develop an AI model to predict survival probabilities based on real data and deploy it as a web service. You will gain hands-on experience in the entire process of AI and web development, from deep learning modeling using PyTorch to building a backend server with FastAPI and implementing a user interface with Next.js.

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

This course consists of 47 lessons, totaling approximately 39 hours, in which you build deep learning from the ground up with PyTorch. There are many deep learning courses, but most fall into one of two categories. Either they skim only the basics of using libraries, leaving you unsure why the code is written the way it is, or they start with equations and leave you giving up halfway through. This course takes a different approach. First, you create a result; then you dig into what happened under the hood. The course begins with pretrained models. You will run image classification, try inpainting with Stable Diffusion, and even practice image captioning with BLIP. Because you produce tangible results from the very first lesson, you gain the motivation to learn the theory that follows. Next comes the core of this course. Tensors are covered over seven lessons. In addition to indexing and broadcasting, you will explore memory structures such as storage and strides, memory contiguity management, GPU operations, and conversions between tensors and NumPy arrays. It is rare to find a Korean-language course that covers tensors in this much depth. Once you understand this part, you will read all subsequent code differently. You will then learn preprocessing that converts real-world data into tensors. The course covers different data types, including image color channels, tabular data and one-hot encoding, time series, and text embeddings. Moving on to the principles of training, you will implement loss functions and gradient descent yourself using numerical differentiation, and then see how Autograd automates the process. Only those who have built it themselves can truly appreciate the convenience of automatic differentiation. The second half focuses on neural network architectures. You will build deep neural networks with nn.Module and classify CIFAR-10, encounter the limitations of fully connected neural networks, and naturally arrive at why CNNs are necessary. Finally, you will move on to text generation, covering everything from bigram models to embeddings and MLP-based generative models, and then the principles of Attention’s Query, Key, and Value. If you know how to use Python and want to properly understand deep learning, what you will take away from this course is not example code, but the ability to read and understand the structure of any model you encounter.

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 is a comprehensive program that covers everything you need to know to utilize generative AI in practical work settings, from the basic principles of LLM (Large Language Models) to RAG (Retrieval-Augmented Generation), and even the latest technologies like AI Agent and MCP (Modular Command Protocol). It is structured to naturally teach you how generative AI understands, searches, judges information, and extends into AI Agents that take action, following the flow of technological development.

📢 "What happens when AI is added to a factory?" 🤖 The era of traditional manufacturing is over; we are now in the age of AI- and data-driven smart factories! For those who have been wondering "what exactly is a smart factory?" and those who want to know "how AI technology is actually applied on the manufacturing floor," we have prepared a very easy-to-understand and clear introductory lecture.

See exam types and trends at a glance! Strengthen your practical skills with approximately 100 questions. The perfect preparation for the AWS AI Practitioner exam, allowing for systematic study even when preparing on your own.

This course explains the basic concepts of LLM and GPT in an easy-to-understand way for everyone. Students will build their own chatbot using the GPT API and deploy it on the web using Streamlit. By experiencing everything from the basics to hands-on practice, you can build a solid foundation for your first steps in utilizing AI.

Do you know the basics but feel overwhelmed when it comes to applying them in practice? Drawing on real-world experience, we make complex LLM architectures easy to understand.

This course is a hands-on masterclass that completely dissects the core technological evolution of generative AI, from LDM (Latent Diffusion Model) to DiT (Diffusion Transformer). We directly analyze the latent space-based learning principles of LDM, the structure of Stable Diffusion, and the implementation methods of the latest Diffusion Transformer through papers and code. Students will systematically learn the latest trends and structural evolution of generative models by directly implementing LDM, CFG (Classifier-Free Guidance), and DiT models using PyTorch.