AI 48
- AI 시대 Game making and playing
- VLM modeling & Training
- [Back to basic] Training
- Liquid AI - LFM2 Paper Review
- Transformer Step3 Vision language Model (VLM)
- Transformer Step4 Multimodal Transformer
- Transformer Step2 Modern LLM
- Transformer - Step1 Basic concepts
- Transformer Step0 - nanoGPT
- Claude - Code (CLI)
- Claude - Intro
- Claude - Cowork (Desktop)
- 🎐 Agentic AI - skills
- 🎐 Agentic AI - runtime, model
- 🎐 Agentic AI - Plug-in, subagents
- 🎐 Agentic AI - Definition & Framework
- 🎐 Agentic AI - advanced reasoning
- 🎐 Agentic AI - tools
- 🎐 Agentic AI - Memory
- 🎐 Agentic AI - design patterns
- Ch4 Statistics - Other Estimation Methods - MAP, Regularization
- Ch4 Statistics - Bayesian Statistics
- Ch4 Statistics - Maximum Likelihood Estimation(MLE)
- Ch3 probability - part3, the exponential Family, multi model
- ch3 probability - part2, Linear Gaussian systems
- ch3 Probability - part1 - Multivariant Gaussian Distribution
- Ch3 Probability - Multi model - GMM
- Sobol Indices
- Random Forest
- Gaussian Process Regression(GRP)
- Self-Supervised Learning
- Recurrent Neural Network(RNN) & LSTM
- Pre-trained Model & Transfer Learning
- Physical Informed Neural Network (PINN) & Operator Learning
- Image Segmentation, Object detection.
- Generative Adversarial Network(GAN)
- Diffusion Model
- Convolutional AutoEncoder(CAE)
- Anomaly Detection
- Ch5 Low Reynolds number flow - Stokes Flow part1
- Ch4 Laminar flow - Hele Shaw flow
- Ch4 Laminar flow - Stoke's First Theorem
- Ch4 Laminar Flow
- Ch4. Gradient decent algorithm
- Ch3_2. Classification_Logistic Regression
- Ch3_1. Classification_Perceptron
- Ch2_1. Regression
- Ch1. Optimization for Deep Learning