Machine Learning with Python
Comprehensive Python tutorial covering machine learning concepts, algorithms, and deep learning with TensorFlow. Includes hands-on projects and real-world applications.
TensorFlow Machine Learning Basic Tutorial
TensorFlow와 머신러닝의 기초를 배우고 실제 프로젝트로 배추 가격 예측 AI를 개발하는 과정. 개발환경 설정부터 선형 회귀, K-평균 알고리즘, 데이터 시각화, 웹 서버 구현까지 다룹니다.
Global Optimization for Cardinality-Constrained Minimum Sum-of-Squares Clustering via Semidefinite Programming
Explore global optimization techniques for cardinality-constrained minimum sum-of-squares clustering using semidefinite programming, focusing on exact algorithms and state-of-the-art approaches.
Practical Clustering and Topological Data Analysis
Explore clustering algorithms and topological data analysis from a data science perspective, covering techniques from k-means to DBSCAN and dimensionality reduction methods like UMAP.
K-Means and K-Medians Under Dimension Reduction
Explore k-means and k-medians clustering algorithms under dimension reduction, examining distortion graphs, cost analysis, and combinatorial lemmas for robust high-dimensional statistics.
Create Your Own Music Recommendation System Using ML
Learn to build a music recommendation system using machine learning, exploring algorithms like K-means, SVM, and neural networks with Spotify's API, Keras, and Sklearn.
Sum-of-Minimum Model: Joint Optimization of Specialized Models for Heterogeneous Data
Explore joint optimization of specialized models for heterogeneous data using the sum-of-minimum approach. Learn algorithm design, performance bounds, and applications in various machine learning tasks.
Fast Streaming Euclidean Clustering with Constant Space
Explore streaming algorithms for k-median and k-means clustering, achieving constant space complexity independent of data size and aspect ratio. Improve space and update time for approximate subspace embeddings.
Exploring Clustering Algorithms in Machine Learning
Dive into clustering algorithms, exploring principles and real-world applications. Learn various types, including K-means, and understand how to implement and evaluate them effectively.
A Nearly Tight Analysis of Greedy k-means++
Explore the greedy k-means++ algorithm's performance, analyzing its approximation guarantees and comparing it to the original k-means++ method for solving the k-means problem.