![[Revised Edition] The Complete Guide to Python Machine Learning강의 썸네일](https://cdn.inflearn.com/public/courses/324238/cover/7e380aa0-48ba-4ee7-a6b2-8da7900568d6/324238-eng.png?f=avif&w=420)
[Revised Edition] The Complete Guide to Python Machine Learning
dooleyz3525
₩77
초급 / Python, Machine Learning(ML), Statistics
4.9
(446)
We will help you easily understand the core concepts of machine learning and acquire the ability to implement practical machine learning applications by moving away from theory-based machine learning courses.
초급
Python, Machine Learning(ML), Statistics
[Revised Edition] The Complete Guide to Python Machine Learning
dooleyz3525
₩77
초급 / Python, Machine Learning(ML), Statistics
4.9
(446)
![[Renewal] First-time Python Machine Learning Bootcamp (Easy! Solving Real Kaggle Problems) [Data Analysis/Science Part2]강의 썸네일](https://cdn.inflearn.com/public/courses/327464/cover/55c4db7b-7764-43cc-ac75-d9e8d08098f1/327464-eng.png?f=avif&w=420)
[Renewal] First-time Python Machine Learning Bootcamp (Easy! Solving Real Kaggle Problems) [Data Analysis/Science Part2]
funcoding
₩59
초급 / Machine Learning(ML), Kaggle, Pandas, Python
4.9
(109)
Unlike existing lectures, this course has been newly designed based on the instructor's initial failures in learning machine learning, enabling easy understanding and application to real-world problems.
초급
Machine Learning(ML), Kaggle, Pandas
[Renewal] First-time Python Machine Learning Bootcamp (Easy! Solving Real Kaggle Problems) [Data Analysis/Science Part2]
funcoding
₩59
초급 / Machine Learning(ML), Kaggle, Pandas, Python
4.9
(109)
Introduction to Machine Learning/Deep Learning and Python Introductory Course for Learning
YoungJea Oh
₩9
입문 / Machine Learning(ML), Deep Learning(DL), Python, Numpy, Pandas, Matplotlib
4.8
(36)
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.
입문
Machine Learning(ML), Deep Learning(DL), Python
Introduction to Machine Learning/Deep Learning and Python Introductory Course for Learning
YoungJea Oh
₩9
입문 / Machine Learning(ML), Deep Learning(DL), Python, Numpy, Pandas, Matplotlib
4.8
(36)
Understanding the Fundamentals and Operating Principles of Generative AI
YoungJea Oh
₩43
7일만
23%
₩33
중급이상 / Generative AI, transformer, multimodal, Python, openai
4.8
(57)
Understand the operating principles of deep learning-based generative AI models and acquire utilization methods through hands-on practice.
중급이상
Generative AI, transformer, multimodal
Understanding the Fundamentals and Operating Principles of Generative AI
YoungJea Oh
₩43
7일만
23%
₩33
중급이상 / Generative AI, transformer, multimodal, Python, openai
4.8
(57)
Introduction to Machine Learning and Deep Learning Using Python
YoungJea Oh
₩52
7일만
23%
₩40
입문 / Machine Learning(ML), Deep Learning(DL), Python, Numpy, Pandas, Matplotlib, Anaconda, Tensorflow, Keras, CNN
4.9
(58)
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.
입문
Machine Learning(ML), Deep Learning(DL), Python
Introduction to Machine Learning and Deep Learning Using Python
YoungJea Oh
₩52
7일만
23%
₩40
입문 / Machine Learning(ML), Deep Learning(DL), Python, Numpy, Pandas, Matplotlib, Anaconda, Tensorflow, Keras, CNN
4.9
(58)
Practical Docker: Creating your own deep learning cloud computer with Docker
danielyouk
₩59
초급 / Docker, Virtualization, Python, Deep Learning(DL), mlops, Azure, Linux, azure-data-lake-gen2
4.7
(14)
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.
초급
Docker, Virtualization, Python
Practical Docker: Creating your own deep learning cloud computer with Docker
danielyouk
₩59
초급 / Docker, Virtualization, Python, Deep Learning(DL), mlops, Azure, Linux, azure-data-lake-gen2
4.7
(14)
![[Python] Easily understand and implement machine learning without math강의 썸네일](https://cdn.inflearn.com/public/courses/328518/cover/5748a5c0-65a6-4d06-bb54-9f3981fafeee/328518-eng.png?f=avif&w=420)
[Python] Easily understand and implement machine learning without math
dlbro
₩52
입문 / Machine Learning(ML), Python, Scikit-Learn, Big Data
4.7
(42)
Learn how to understand advanced machine learning models without math and implement them easily in Python.
입문
Machine Learning(ML), Python, Scikit-Learn
[Python] Easily understand and implement machine learning without math
dlbro
₩52
입문 / Machine Learning(ML), Python, Scikit-Learn, Big Data
4.7
(42)
AI-based recommendation system
YoungJea Oh
₩39
7일만
20%
₩31
중급이상 / Deep Learning(DL), Machine Learning(ML), Recommendation System, Python, Tensorflow, kakao-tech
4.4
(8)
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!
중급이상
Deep Learning(DL), Machine Learning(ML), Recommendation System
AI-based recommendation system
YoungJea Oh
₩39
7일만
20%
₩31
중급이상 / Deep Learning(DL), Machine Learning(ML), Recommendation System, Python, Tensorflow, kakao-tech
4.4
(8)
From Introduction to Reinforcement Learning to Deep Q-learning/Policy Gradient
YoungJea Oh
₩43
7일만
17%
₩35
중급이상 / Deep Learning(DL), Reinforcement Learning(RL), Python, PyTorch
4.6
(31)
All the recent amazing achievements in the field of artificial intelligence have been announced in the field of reinforcement learning. We have covered reinforcement learning technology, which is revolutionizing true artificial intelligence technology such as robots, autonomous driving technology, and human-like machines, from the basics to the advanced level, in an easy-to-understand way for beginners.
중급이상
Deep Learning(DL), Reinforcement Learning(RL), Python
From Introduction to Reinforcement Learning to Deep Q-learning/Policy Gradient
YoungJea Oh
₩43
7일만
17%
₩35
중급이상 / Deep Learning(DL), Reinforcement Learning(RL), Python, PyTorch
4.6
(31)
Triangles in action! OpenAI Triton beginner
triangle
₩116
초급 / Deep Learning(DL), Python, gpgpu, triton, openai, kernel-programming, CNN, dropout, relu, argmax
4.1
(7)
In this course, you will learn how to program kernels and develop PyTorch modules. You can use the knowledge you have gained to develop models faster.
초급
Deep Learning(DL), Python, gpgpu
Triangles in action! OpenAI Triton beginner
triangle
₩116
초급 / Deep Learning(DL), Python, gpgpu, triton, openai, kernel-programming, CNN, dropout, relu, argmax
4.1
(7)
Learn PyTorch Deep Learning Basics in 6 Days
hanky7479821
₩34
초급 / PyTorch, Deep Learning(DL), CNN, Python
5.0
(28)
If you invest 3 hours a day for 6 days, you can learn the basics of deep learning using PyTorch. Let's get started!
초급
PyTorch, Deep Learning(DL), CNN
Learn PyTorch Deep Learning Basics in 6 Days
hanky7479821
₩34
초급 / PyTorch, Deep Learning(DL), CNN, Python
5.0
(28)
![[PyTorch] Deep Learning Leading to Practical AI - From Basics to Paper Implementation강의 썸네일](https://cdn.inflearn.com/public/courses/326248/cover/945f4b20-e4e1-4d10-8e64-f2757c01736b?f=avif&w=420)
[PyTorch] Deep Learning Leading to Practical AI - From Basics to Paper Implementation
dlbro
₩57
초급 / Deep Learning(DL), Python, PyTorch, Artificial Neural Network, Machine Learning(ML)
4.6
(78)
This is a lecture on implementing various artificial neural networks using PyTorch, a deep learning framework with very high utility in the field of artificial intelligence.
초급
Deep Learning(DL), Python, PyTorch
[PyTorch] Deep Learning Leading to Practical AI - From Basics to Paper Implementation
dlbro
₩57
초급 / Deep Learning(DL), Python, PyTorch, Artificial Neural Network, Machine Learning(ML)
4.6
(78)
![[AI Practice] Getting Started with Paper Implementation for AI Research Engineers with PyTorch강의 썸네일](https://cdn.inflearn.com/public/courses/334289/cover/b8885796-1e67-4983-9432-bcda0daae927/334289.png?f=avif&w=420)
[AI Practice] Getting Started with Paper Implementation for AI Research Engineers with PyTorch
whitebox
₩43
중급이상 / Deep Learning(DL), Generative AI, PyTorch, Computer Vision(CV), Python
5.0
(32)
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!
중급이상
Deep Learning(DL), Generative AI, PyTorch
[AI Practice] Getting Started with Paper Implementation for AI Research Engineers with PyTorch
whitebox
₩43
중급이상 / Deep Learning(DL), Generative AI, PyTorch, Computer Vision(CV), Python
5.0
(32)
(First Steps in Deep Learning Modeling) Master the Core Theory of Deep Learning from Gradient Descent to Backpropagation with Formulas and Code!
fasoft
₩59
9일만 얼리버드
68%
₩19
입문 / Python, Tensorflow, AI, Numpy, Deep Learning(DL)
Based on over 100 deep learning training sessions, this course systematically organizes the core foundational theories that students found most challenging. The course connects mathematical intuition, model learning principles, and code implementation step by step in a way that even non-majors can understand, deeply covering the fundamental structure and operating principles of how AI models learn, rather than just library usage. This is a practical introductory course designed to help you grow into a skilled engineer who understands AI principles by implementing core deep learning foundational technologies such as gradient descent, loss functions, optimization, perceptrons, multilayer neural networks, and backpropagation through both formulas and code.
입문
Python, Tensorflow, AI
(First Steps in Deep Learning Modeling) Master the Core Theory of Deep Learning from Gradient Descent to Backpropagation with Formulas and Code!
fasoft
₩59
9일만 얼리버드
68%
₩19
입문 / Python, Tensorflow, AI, Numpy, Deep Learning(DL)
Machine Learning + Deep Learning with Python (From machine learning using sklearn to deep learning development using TensorFlow and Keras)
ilifo
₩26
초급 / Python, sklearn, Machine Learning(ML), Deep Learning(DL), Keras
3.8
(8)
From the basics of machine learning & deep learning! Creating and utilizing classification/regression/clustering/artificial neural networks with Python
초급
Python, sklearn, Machine Learning(ML)
Machine Learning + Deep Learning with Python (From machine learning using sklearn to deep learning development using TensorFlow and Keras)
ilifo
₩26
초급 / Python, sklearn, Machine Learning(ML), Deep Learning(DL), Keras
3.8
(8)
Python Machine Learning
Masocampus
₩54
중급이상 / Machine Learning(ML), Pandas, Numpy, Scikit-Learn, Seaborn, Matplotlib, Python
4.8
(9)
A course to understand and practice machine learning that efficiently performs complex analysis and prediction! Machine learning, which seemed difficult and complicated, can be easily and enjoyably learned in its core with Maso Campus' <Python Machine Learning>.
중급이상
Machine Learning(ML), Pandas, Numpy
Python Machine Learning
Masocampus
₩54
중급이상 / Machine Learning(ML), Pandas, Numpy, Scikit-Learn, Seaborn, Matplotlib, Python
4.8
(9)
Deep Learning Using Python
SW School
₩102
초급 / Python, Keras, Deep Learning(DL)
3.7
(3)
Learn how to use deep learning with Python and Keras.
초급
Python, Keras, Deep Learning(DL)
Deep Learning Using Python
SW School
₩102
초급 / Python, Keras, Deep Learning(DL)
3.7
(3)
Reinforcement Learning All-in-One: Fundamentals, Latest Algorithms, and Practical Applications
multicoreit
₩52
중급이상 / Python, Artificial Neural Network, Reinforcement Learning(RL), Fine-Tuning, optimization-problem
5.0
(6)
AI that thinks and adapts? Now you can build it yourself. Master the core of reinforcement learning with intuitive explanations and code, not complex theories, and grow into a 'practical developer' through finance and industry projects.
중급이상
Python, Artificial Neural Network, Reinforcement Learning(RL)
Reinforcement Learning All-in-One: Fundamentals, Latest Algorithms, and Practical Applications
multicoreit
₩52
중급이상 / Python, Artificial Neural Network, Reinforcement Learning(RL), Fine-Tuning, optimization-problem
5.0
(6)
Data Analysis Basics: Python Fundamentals and Practice for Non-Majors
algolearn
무료
입문 / Python, Machine Learning(ML), Big Data, Coding Test, AI
This is a basic Python course for starters who want to grow as data analysts. - Those who want to do data analysis but don't know Python - Those who want to learn machine learning with Python - Those preparing for the Big Data Analytics Engineer practical exam - Those who want to develop machine learning-based applications with Python
입문
Python, Machine Learning(ML), Big Data
Data Analysis Basics: Python Fundamentals and Practice for Non-Majors
algolearn
무료
입문 / Python, Machine Learning(ML), Big Data, Coding Test, AI
LangGraph in Action: Develop Advanced AI Agents with LLMs
Markus Lang
₩22
얼리버드
30%
₩15
초급 / Python, FastAPI, LangChain, AI Agent, LangGraph
What to Expect from This Course Welcome to LangGraph in Action, your ultimate guide to mastering the design and deployment of advanced AI agents using LangGraph. In this course, you’ll explore the fundamentals of building modular, scalable, and production-ready agents, all with a hands-on approach. From understanding the basics of LangGraph’s state-based design to creating a full-stack application, you’ll gain the skills needed to bring AI agents to life. Course Highlights State-Based Design: Dive into LangGraph’s core philosophy of nodes and edges to create structured, maintainable agents. Memory Management: Explore short-term memory with checkpointers and long-term memory with the Store object to enable agents that adapt and learn. Advanced Workflows: Build human-in-the-loop systems, implement parallel execution, and master multi-agent patterns. Production-Ready Development: Learn asynchronous operations, subgraphs, and create full-stack applications using FastAPI and Docker. By the end of the course, you’ll not only have a strong theoretical understanding but also the practical skills to deploy AI agents anywhere, entirely with open-source tools. Whether you're a developer aiming to stay ahead of the curve or a seasoned engineer looking to expand your AI toolkit, this course equips you for the rapidly growing field of AI agents. With the increasing adoption of AI agents in real-world applications, this course ensures you're prepared to design, build, and deploy advanced systems that solve practical challenges. Let’s start building and shaping the future of AI together!
초급
Python, FastAPI, LangChain
LangGraph in Action: Develop Advanced AI Agents with LLMs
Markus Lang
₩22
얼리버드
30%
₩15
초급 / Python, FastAPI, LangChain, AI Agent, LangGraph
![[Complete NLP Mastery I] The Birth of Attention: Understanding NLP from RNN·Seq2Seq Limitations to Implementing Attention강의 썸네일](https://cdn.inflearn.com/public/files/courses/339625/cover/01ka39v05h3st4hcttnfhfwvhd?f=avif&w=420)
[Complete NLP Mastery I] The Birth of Attention: Understanding NLP from RNN·Seq2Seq Limitations to Implementing Attention
Sotaaz
₩39
입문 / Python, Deep Learning(DL), PyTorch, attention-model, transformer
We understand why Attention was needed and how it works by 'implementing it directly with code'. This lecture starts from the structural limitations of RNN and Seq2Seq models, experimentally verifies the information bottleneck problem and long-term dependency issues created by fixed context vectors, and naturally explains how Attention emerged to solve these limitations. Rather than simply introducing concepts, we directly confirm RNN's structural limitations and Seq2Seq's information bottleneck problems through experiments, and implement **Bahdanau Attention (additive attention)** and **Luong Attention (dot-product attention)** one by one to clearly understand their differences. Each attention mechanism forms Query–Key–Value relationships in what way, has what mathematical and intuitive differences in the weight calculation process, and why it inevitably led to later models naturally connects to their characteristics and evolutionary flow. We learn how Attention views sentences and words, and how each word receives importance weighting to integrate information in a form where formula → intuition → code → experiment are connected as one. This lecture is a process of building 'foundational strength' to properly understand Transformers, helping you deeply understand why the concept of Attention was revolutionary, and why all subsequent state-of-the-art NLP models (Transformer, BERT, GPT, etc.) adopt Attention as a core component. This lecture is optimized for learners who want to embody the flow from RNN → Seq2Seq → Attention not through concepts but through code and experiments.
입문
Python, Deep Learning(DL), PyTorch
[Complete NLP Mastery I] The Birth of Attention: Understanding NLP from RNN·Seq2Seq Limitations to Implementing Attention
Sotaaz
₩39
입문 / Python, Deep Learning(DL), PyTorch, attention-model, transformer
Learn LLM and GPT Basics in 1 Hour
Essential
₩10
초급 / Python, AI, ChatGPT, Generative AI, Deep Learning(DL)
4.3
(3)
This course explains the basic concepts of LLM and GPT in an easy-to-understand way for everyone. Students can directly use the GPT API to create chatbots and run them on the web using Streamlit. By experiencing everything from basics to hands-on practice, you can solidly establish your first steps in AI utilization.
초급
Python, AI, ChatGPT
Learn LLM and GPT Basics in 1 Hour
Essential
₩10
초급 / Python, AI, ChatGPT, Generative AI, Deep Learning(DL)
4.3
(3)
The Secret of Algorithmic Trading: How AI Predicts Stock Prices
cheatkeylab
₩37
초급 / Deep Learning(DL), Python, transformer, lstm, Financial Technology
5.0
(25)
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!
초급
Deep Learning(DL), Python, transformer
The Secret of Algorithmic Trading: How AI Predicts Stock Prices
cheatkeylab
₩37
초급 / Deep Learning(DL), Python, transformer, lstm, Financial Technology
5.0
(25)
Machine Learning, Deep Learning Basics with Python, Keras
pnuswedu
무료
초급 / Machine Learning(ML), Deep Learning(DL), Keras, Python
4.8
(8)
Let's understand model algorithms for machine learning and deep learning by utilizing Keras!
초급
Machine Learning(ML), Deep Learning(DL), Keras
Machine Learning, Deep Learning Basics with Python, Keras
pnuswedu
무료
초급 / Machine Learning(ML), Deep Learning(DL), Keras, Python
4.8
(8)
Deep Learning and TensorFlow for Beginners: The Basics Fast
Jiwoon Jeong
₩9
초급 / Deep Learning(DL), Tensorflow, Python, colab
4.9
(45)
This is a lecture where you can learn Deep Learning using TensorFlow. Learn linear regression, logistic regression, and softmax regression models, and finally create an MLP model that can classify fashion images!
초급
Deep Learning(DL), Tensorflow, Python
Deep Learning and TensorFlow for Beginners: The Basics Fast
Jiwoon Jeong
₩9
초급 / Deep Learning(DL), Tensorflow, Python, colab
4.9
(45)
![[AI Practice] Understanding Diffusion Models through Prompt-to-prompt Paper Implementation강의 썸네일](https://cdn.inflearn.com/public/files/courses/339089/cover/01k7chg2b60npdvpaykrypzmk7?f=avif&w=420)
[AI Practice] Understanding Diffusion Models through Prompt-to-prompt Paper Implementation
dongdong1
₩31
중급이상 / Python, Deep Learning(DL), PyTorch, AI, Generative AI
5.0
(6)
This course conducts hands-on practice related to Diffusion models among generative artificial intelligence models. By reading and implementing the prompt-to-prompt paper, which is a representative Diffusion model application paper, we expect to cultivate the ability to understand the latest artificial intelligence papers.
중급이상
Python, Deep Learning(DL), PyTorch
[AI Practice] Understanding Diffusion Models through Prompt-to-prompt Paper Implementation
dongdong1
₩31
중급이상 / Python, Deep Learning(DL), PyTorch, AI, Generative AI
5.0
(6)
The Magic of 5 Lines of Code, 5-Line Machine Learning PyCaret: Adding AutoML Wings to Data Analysis Projects
haeyeomiso
₩39
초급 / Python, automl, Machine Learning(ML), mlops, AI
4.5
(2)
Hello! Have you ever gotten tired of repeatedly writing the same code over and over again while doing data analysis? 😫 Now let's give your projects wings with PyCaret! 🚀 This course will teach you the secret to automating most of the tedious and complex machine learning processes with just 5 lines of code. Here's how this course will transform you! ✨ Master Real-World Projects: 📈Customer purchase prediction (classification), 📉Medical cost prediction (regression), 🧩Customer grouping (clustering), ⏳Airline passenger prediction (time series)! You can develop 'real' problem-solving skills by following along with 4 core projects hands-on. Acquire Expert Skills: But that's not all! 🤖 We'll also teach you expert skills like managing your models with MLOps and creating API servers with just a few clicks. Pretty amazing, right? Protect Your Work-Life Balance: Most importantly, we'll drastically reduce your repetitive tasks and help you focus only on core analysis. Leaving work on time is a bonus! 😉 Whether you're not yet comfortable with coding or you're a current professional wanting to explosively boost productivity, everyone is welcome! 👋 Shall we transform together into a '10x faster data scientist'?
초급
Python, automl, Machine Learning(ML)
The Magic of 5 Lines of Code, 5-Line Machine Learning PyCaret: Adding AutoML Wings to Data Analysis Projects
haeyeomiso
₩39
초급 / Python, automl, Machine Learning(ML), mlops, AI
4.5
(2)
Creating Custom LLMs: From Basic RAG Concepts to Multimodal·Agent Practice for Beginners
HappyAI
₩18
입문 / Python, vector-database, LLM, LangChain, RAG
4.8
(13)
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.
입문
Python, vector-database, LLM
Creating Custom LLMs: From Basic RAG Concepts to Multimodal·Agent Practice for Beginners
HappyAI
₩18
입문 / Python, vector-database, LLM, LangChain, RAG
4.8
(13)
Mastering Model Context Protocol (MCP): A Practical Guide
Markus Lang
₩25
얼리버드
26%
₩19
중급이상 / Python, FastAPI, oauth2, LangGraph, Model Context Protocol
Mastering Model Context Protocol (MCP) is a practical, engineering-focused course designed to help developers build real, secure, and production-ready AI backends. After helping thousands of students overcome confusion around LLM integration, tool calling, and backend architecture, I created this course to solve the most common problems: “How do I build a reliable backend that LLMs can call safely?” “How do I choose between SSE, stdio, or streamable-http?” “How do I scale MCP into real applications with FastAPI, Auth0, and LangGraph?” “How do I structure my MCP tools, resources, prompts, and context?” In this course, I guide you step-by-step—from spinning up a minimal MCP server to deploying a fully secure, Dockerized system. Every lesson is hands-on, designed to remove complexity and give you a clear, repeatable workflow for building modern AI systems. If you're frustrated by vague tutorials and want a clear, concrete, engineering-level understanding of MCP, this course is built for you.
중급이상
Python, FastAPI, oauth2
Mastering Model Context Protocol (MCP): A Practical Guide
Markus Lang
₩25
얼리버드
26%
₩19
중급이상 / Python, FastAPI, oauth2, LangGraph, Model Context Protocol
Pixart & SANA, Complete Mastery of Diffusion III: Learning Through Implementation
Sotaaz
₩69
중급이상 / Python, PyTorch, AI
We implement the latest Transformer-based PixArt and lightweight adaptation SANA step by step from theory to code. Building on DDPM·DDIM·LDM·DiT covered in Parts I·II, we complete hands-on practice including text encoder integration, samplers (DDIM/ODE), v-prediction/CFG tuning, and small-scale data style fine-tuning.
중급이상
Python, PyTorch, AI
Pixart & SANA, Complete Mastery of Diffusion III: Learning Through Implementation
Sotaaz
₩69
중급이상 / Python, PyTorch, AI
Artificial Intelligence with Python
hjk1000
₩13
초급 / Python, Numpy, Tensorflow, Matplotlib
Deep learning is a technology that learns data through neural networks composed of combinations of complex functions. In this lecture, we will mathematically understand the core concepts of deep learning and analyze them from the perspective of matrix operations. In particular, utilizing Python's NumPy library, we will visually examine how parameters are updated by directly implementing the forward and backward propagation processes of deep learning. Even the seemingly complex neural network structure becomes clear when analyzed with matrix operations. This lecture focuses more on understanding concepts than coding and is suitable for students who wish to intuitively grasp the principles of deep learning mathematically.
초급
Python, Numpy, Tensorflow
Artificial Intelligence with Python
hjk1000
₩13
초급 / Python, Numpy, Tensorflow, Matplotlib
From LDM to DiT, Complete Mastery of Diffusion Through Implementation II
Sotaaz
₩51
초급 / Python, Deep Learning(DL), Stable Diffusion, AI
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.
초급
Python, Deep Learning(DL), Stable Diffusion
From LDM to DiT, Complete Mastery of Diffusion Through Implementation II
Sotaaz
₩51
초급 / Python, Deep Learning(DL), Stable Diffusion, AI
What if I had been on the Titanic?! Building an AI Web Service to Predict Survival Probability with PyTorch & Next.js
dakgangjung123
₩102
중급이상 / Python, Deep Learning(DL), PyTorch, Next.js, FastAPI
5.0
(1)
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.
중급이상
Python, Deep Learning(DL), PyTorch
What if I had been on the Titanic?! Building an AI Web Service to Predict Survival Probability with PyTorch & Next.js
dakgangjung123
₩102
중급이상 / Python, Deep Learning(DL), PyTorch, Next.js, FastAPI
5.0
(1)
Free Python Course (Usage 7) - Machine Learning
nadocoding
무료
초급 / Machine Learning(ML), Scikit-Learn, Python, Anaconda
4.9
(63)
It's finally here. The world's easiest machine learning course. It's free, so why hesitate? Just keep pressing the 'Learn' button.
초급
Machine Learning(ML), Scikit-Learn, Python
Free Python Course (Usage 7) - Machine Learning
nadocoding
무료
초급 / Machine Learning(ML), Scikit-Learn, Python, Anaconda
4.9
(63)
![[Tensorflow2] Complete conquest of Python deep learning - Latest techniques of GAN, BERT, RNN, CNN강의 썸네일](https://cdn.inflearn.com/public/courses/324484/course_cover/f6f03229-d156-4a27-a0f2-ede2361c586f/sjh_Deep_Learning.png?f=avif&w=420)
[Tensorflow2] Complete conquest of Python deep learning - Latest techniques of GAN, BERT, RNN, CNN
nomad
₩26
중급이상 / Tensorflow, Python, Deep Learning(DL), Keras
3.4
(18)
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.
중급이상
Tensorflow, Python, Deep Learning(DL)
[Tensorflow2] Complete conquest of Python deep learning - Latest techniques of GAN, BERT, RNN, CNN
nomad
₩26
중급이상 / Tensorflow, Python, Deep Learning(DL), Keras
3.4
(18)
AI Introduction Study Method by a Graduate Student Who Wrote 2 Papers as an Undergraduate
epoch
무료
입문 / AI, Linear Algebra, Probability and Statistics, Python
4.8
(38)
Introducing AI entry-level study methods for undergraduates.
입문
AI, Linear Algebra, Probability and Statistics
AI Introduction Study Method by a Graduate Student Who Wrote 2 Papers as an Undergraduate
epoch
무료
입문 / AI, Linear Algebra, Probability and Statistics, Python
4.8
(38)
Machine Learning Theory and Python Practice
Minsuk Heo
무료
초급 / Python, Machine Learning(ML)
4.7
(150)
This is a comprehensive lecture on machine learning. It consists of short and easy-to-understand video lectures based on practical experience.
초급
Python, Machine Learning(ML)
Machine Learning Theory and Python Practice
Minsuk Heo
무료
초급 / Python, Machine Learning(ML)
4.7
(150)
