Trí tuệ nhân tạo với Python

Why this course is special: Key Advantages • Intuitive Visualization: Directly observe algorithm operations in real-time with Pygame 2D simulations • Practical Implementation Experience: Go beyond theory and internalize autonomous driving algorithms by coding directly • Master Core Algorithms: Focused learning of essential algorithms such as Dijkstra, Pure Pursuit, ICP, etc. • Step-by-step Advanced Learning: Systematic difficulty progression from basics to SLAM • Lidar-based SLAM: Practical map building and localization in unknown environments

Let's visually learn the principles of deep learning using Excel.

Implementing deep learning basics with Excel to deeply understand their meaning! Learn how image data is processed!

Implement Deep Learning Basics in Excel for Deeper Understanding!

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

Deeply understand deep learning basics by implementing in Excel!

Pre-Trained Model (VGG16) / CAM / Grad-CAM / YOLO / STYLE TRANSFER / PIX2PIX / CYCLEGAN Implementing Stable Diffusion Directly

Hot image generation model! Let's understand it by implementing it from the basics! Let's implement AUTO ENCODER / VAE / GAN / DIFFUSION together!

Why this course is special: Key Advantages • Intuitive Visualization: Directly observe algorithm operations in real-time with Pygame 2D simulations • Practical Implementation Experience: Go beyond theory and internalize autonomous driving algorithms by coding directly • Master Core Algorithms: Focused learning of essential algorithms such as Dijkstra, Pure Pursuit, ICP, etc. • Step-by-step Advanced Learning: Systematic difficulty progression from basics to SLAM • Lidar-based SLAM: Practical map building and localization in unknown environments

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.

If you invest 3 hours a day for 6 days, you can learn the basics of deep learning using PyTorch. Let's get started!

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!

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

Leverage Python to learn machine learning techniques, improving real data information extraction and prediction model development skills!

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.

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.

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.