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What is H2O AutoML? Train ML Models Without Coding H2O AutoML is an open-source tool that automates the machine learning process—from data preprocessing to model training, tuning, and selection. It’s built by H2O.ai and is designed for both beginners and experts who want to save time and get powerful models without diving deep into code. Whether you’re analyzing customer churn or predicting sales, H2O AutoML can help you build production-ready models in minutes. ...

What is LangChain? Build LLM Apps Easily with Python LangChain is an open-source Python framework designed to make it easy to build applications powered by large language models (LLMs) like ChatGPT. It provides components to manage prompts, chain logic, memory, tools, and more. Whether you’re building a chatbot, a document search tool, or an agent that uses tools like calculators or web search, LangChain simplifies the process. Table of Contents Why LangChain? Imagine you want to build a customer support chatbot that can: ...

What is AutoML? Learn Automated Machine Learning with Python AutoML (Automated Machine Learning) refers to technologies that automate the entire machine learning pipeline, including data preprocessing, model selection, hyperparameter tuning, and evaluation. With AutoML, even beginners can build accurate ML models without deep technical expertise. Table of Contents Why AutoML? Imagine you’re running a coffee shop and want to predict which customers are likely to order an Americano. Building a machine learning model from scratch would require: ...

What is EDA (Exploratory Data Analysis)? Analyzing data is a lot like cooking. EDA (Exploratory Data Analysis) is the part where you unpack your ingredients, check what’s fresh, what’s expired, and how much you have—before you start cooking. If you skip this step, your final dish (aka, your machine learning model) might be bland, undercooked, or even dangerous. Another real-life analogy? A health check-up. Just like you wouldn’t prescribe medicine without first examining a patient’s condition, you shouldn’t build a model without first understanding your data. EDA gives you the insight you need to clean, prepare, and model your data wisely. ...

What is Scikit-Learn (sklearn)? Scikit-learn, often imported as sklearn, is one of the most popular and powerful machine learning libraries in Python. It provides a wide range of tools for building, training, and evaluating models—from simple regression to advanced ensemble techniques. Whether you’re a beginner experimenting with classification or a data scientist fine-tuning pipelines, scikit-learn offers a consistent and easy-to-use API across algorithms. Table of Contents Key Features of Scikit-Learn Wide Algorithm Support Linear Regression, Logistic Regression, Decision Trees, Random Forests, SVM, KNN, and more. Preprocessing Tools Scaling, normalization, encoding, missing value imputation. Model Selection Cross-validation, GridSearchCV, RandomizedSearchCV for hyperparameter tuning. Pipelines Combine preprocessing and modeling into a single workflow. Clustering and Dimensionality Reduction KMeans, PCA, DBSCAN, and other unsupervised learning techniques. Simple Example: Classification with Scikit-Learn Here’s a basic example using the Iris dataset and a Support Vector Machine (SVM): ...

What is Lasso Regression? Lasso Regression is an extension of linear regression designed to reduce overfitting and improve model simplicity. It’s especially helpful when dealing with high-dimensional data by automatically eliminating irrelevant features, making the model more interpretable and efficient. Table of Contents Concept of Lasso Regression Lasso stands for Least Absolute Shrinkage and Selection Operator. It is a linear regression model that includes an L1 regularization term to penalize large coefficients. ...

What is RNN (Recurrent Neural Network)? In many real-world applications, especially in natural language processing and time series prediction, data comes in sequences. Unlike traditional neural networks, which treat each input independently, RNNs are designed to handle sequential data by maintaining memory of previous inputs. This makes them useful for understanding the context in tasks like speech recognition, language modeling, and more. Table of Contents Concept of RNN RNN stands for Recurrent Neural Network. It is a type of neural network where connections between nodes form a directed cycle. This cycle allows information to persist, making it ideal for sequential data. ...

1. FAISS란? FAISS(Facebook AI Similarity Search)는 Facebook AI Research에서 개발한 **고차원 벡터 유사성 검색과 최근접 이웃 탐색(Nearest Neighbor Search, NNS)**을 위한 라이브러리입니다. 벡터 데이터를 효율적으로 처리하고, 대규모 데이터셋에서 빠른 검색 성능을 제공하도록 설계되었습니다. FAISS는 특히 고차원 벡터를 다루는 머신러닝 및 딥러닝 응용 프로그램에서 중요한 역할을 합니다. 이미지 검색, 자연어 처리(NLP), 추천 시스템 등에서 널리 사용됩니다. Table of Contents 2. 주요 특징 1) 빠른 유사성 검색 고차원 데이터에서 벡터 간의 유사성을 빠르게 계산. 유클리디안 거리(Euclidean Distance), 코사인 유사도(Cosine Similarity) 등 다양한 거리 측정을 지원. 2) 효율적인 인덱싱 IVF (Inverted File Index), **PQ (Product Quantization)**와 같은 고급 인덱싱 기법을 사용하여 검색 속도를 최적화. GPU 가속을 통해 대규모 데이터셋에서도 실시간 검색이 가능. 3) 확장성 싱글 머신에서 수십억 개의 벡터를 처리할 수 있는 확장성을 가짐. CPU와 GPU 모두 지원. 4) 오픈소스 Apache 2.0 라이선스로 무료로 제공되며, Python 및 C++에서 사용 가능. 3. FAISS의 작동 원리 FAISS는 기본적으로 다음 두 가지 단계로 작동합니다: ...

1. 코사인 유사도란? 코사인 유사도(Cosine Similarity)는 두 벡터 간의 **각도(Cosine of the Angle)**를 기반으로 유사성을 측정하는 방법입니다. 벡터의 크기(길이)가 아니라 방향에 초점을 맞추기 때문에, 데이터의 크기가 다르더라도 유사성을 계산하는 데 유용합니다. 코사인 유사도의 값은 -1부터 1 사이의 범위를 가지며: 1: 두 벡터가 완전히 같은 방향을 가짐 (가장 유사). 0: 두 벡터가 서로 직각(독립적). -1: 두 벡터가 반대 방향을 가짐 (가장 비유사). Table of Contents 2. 코사인 유사도 공식 두 벡터 $𝐴$와 $𝐵$의 코사인 유사도는 다음과 같이 계산됩니다: ...

1. 벡터 데이터베이스란? 벡터 데이터베이스(Vector Database)는 고차원 벡터 데이터를 저장하고 빠르게 검색할 수 있도록 설계된 데이터베이스입니다. 전통적인 관계형 데이터베이스(RDBMS)나 NoSQL 데이터베이스와는 달리, 벡터 데이터베이스는 유사도 검색과 **최근접 이웃 탐색(Nearest Neighbor Search, NNS)**을 최적화하여 대량의 벡터 데이터에서 가장 유사한 항목을 빠르게 찾는 데 특화되어 있습니다. 벡터란? 벡터는 숫자로 이루어진 배열이며, 특정 객체의 특성을 수학적으로 표현합니다. 벡터는 딥러닝 및 머신러닝 모델에서 주로 생성되며, 이미지, 텍스트, 오디오, 동영상 등 다양한 데이터 유형을 숫자 벡터로 변환합니다. ...