Showing 289–312 of 540 articles, newest first.
What is random forest? Random Forest is an ensemble learning algorithm that trains many decision trees independently and combines their predictions (by voting or averaging). This guide: a clear definition, how it works, bagging, feature importance, classification and regression, real-world examples, its difference from a single decision tree, its limits, and FAQs.
What is prompt injection? Prompt injection is an AI security vulnerability where hidden instructions are inserted into a language model's input to make the model break the developer's rules. This guide: a clear definition, how prompt injection works, direct and indirect injection, its difference from jailbreak, real-world examples, LLM security, defense methods, and FAQs.
What is overfitting? Overfitting is when a machine learning model memorizes the training data and fails on new data. This guide: a clear definition, how to detect overfitting, the difference from underfitting, regularization, cross validation, generalization, real-world examples, and FAQs.
What is Ollama? Ollama is an open-source tool that lets you download and run large language models on your own computer or server with a single command. This guide: a clear definition, how Ollama works, running LLMs locally, GGUF and the model library, hardware requirements, Ollama vs cloud API, and FAQs.
What is OCR? OCR (Optical Character Recognition) is the technology that converts the text inside an image or scanned document into machine-editable, searchable text. This guide: a clear definition, why OCR matters, how it works, its types, document digitization, handwriting recognition, intelligent document processing, Turkish OCR, KVKK, and FAQs.
What is Microsoft Copilot? Microsoft Copilot is an AI assistant that embeds large language models into Microsoft products like Word, Excel, Outlook, Teams, and Windows to boost office productivity. This guide: a clear definition, M365 integration, how it works, Copilot types, licensing, KVKK and data security, the difference from ChatGPT, limits, and FAQs.
What is text summarization? Text summarization is a natural language processing task that reduces a long text into a shorter form while preserving its main idea and key information. This guide: a clear definition, extractive vs abstractive summarization, how LLM summarization works, document analysis use cases, KVKK, limits, and FAQs.
What is LoRA? LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning method that adapts a large language model by adding small, trainable matrices next to its frozen weights instead of changing all of them. This guide: a clear definition, how LoRA works, QLoRA and variants, the adapter idea, fine-tuning cost, comparison with full fine-tuning, and FAQs.
What is LLM observability? LLM observability is the practice of tracing every request of a language model application end to end, making prompts, responses, latency, cost, and quality visible. This guide: a clear definition, why it matters, how tracing works, Langfuse and OpenTelemetry, production monitoring metrics, evaluation, KVKK, and FAQs.
What is LLM evaluation? LLM evaluation (eval) is the systematic measurement of a large language model's or LLM-based application's outputs for accuracy, consistency and safety. This guide: a clear definition, why it matters, evaluation metrics, LLM as a judge, benchmarks, ragas, offline vs online eval, KVKK, and FAQs.
What is Llama? Llama is a family of large language models (LLMs) developed by Meta whose model weights are released openly to everyone. This guide: a clear definition, how Llama works, what an open-weight model means, versions and variants, running it as a local LLM, the Llama license, enterprise and Türkiye use cases, KVKK, comparison with closed models, and FAQs.
What is speech recognition? Speech recognition (ASR, Automatic Speech Recognition) is the AI technology that lets a device or software automatically convert spoken audio into written text. This guide: a clear definition, how ASR works, its link to speech to text, models like Whisper, word error rate, call center analytics, KVKK, and FAQs.
What is personal data? Personal data is any information relating to an identified or identifiable natural person. This guide: a clear definition, why personal data matters, the scope of Türkiye's KVKK, special categories of personal data, data processing, the disclosure obligation, its relationship with AI and LLMs, limits, and FAQs.
What is predictive maintenance? Predictive maintenance is an approach that analyzes sensor data collected from machines with artificial intelligence to forecast when a piece of equipment will fail and to schedule maintenance just in time. This guide: a clear definition, how it works, failure prediction, sensor data and time-series analysis, the difference from preventive maintenance, industry examples, maintenance cost, and FAQs.
What is a decision tree? A decision tree is a rule-based, interpretable machine learning model that performs classification or regression by splitting data through a series of yes/no questions. This guide: a clear definition, how a decision tree works, information gain and gini, pruning, overfitting, its relationship to random forest, sector examples, and FAQs.
What is K-Means? K-Means clustering is an unsupervised machine learning algorithm that splits unlabeled data into K groups of similar points. This guide: a clear definition, how K-Means works, choosing K with the elbow method, the centroid idea, customer segmentation examples, K-Means vs hierarchical clustering, its limits, and FAQs.
What is a Transformer? The Transformer architecture is an attention-based AI architecture that processes a text not word by word but all pieces at once, through their relationships to one another. This guide: a clear definition, the attention mechanism, the encoder-decoder structure, positional encoding, its link to LLM architecture, real-world examples, limits, and FAQs.
What is alignment? Alignment is the effort to make an AI system's goals, behaviors, and outputs consistent with people's true intent and values. This guide: a clear definition, why it matters, how it works (RLHF and Constitutional AI), value alignment, AI safety, reward hacking, Türkiye and enterprise examples, comparisons, and FAQs.
What is GEO? GEO (Generative Engine Optimization) is the discipline of optimizing content to increase its chance of being cited as a source in the answers of generative AI engines like ChatGPT, Perplexity, and Gemini. This guide: a clear definition, how GEO works, the difference from SEO, citability, AI assistant visibility, llms.txt, Türkiye examples, and FAQs.
What is Gemini? Gemini is Google's family of multimodal AI models that can process text, images, audio, and code in a single model. This guide: a clear definition, how Gemini works, model variants, Gemini features, Workspace integration, comparison with ChatGPT, data privacy, and FAQs.
What is GDPR? GDPR (General Data Protection Regulation) is the EU's comprehensive data protection regulation governing the processing of personal data and binding every organization that serves people in the EU. This guide: a clear definition, how GDPR works, its core principles, the difference from KVKK, the right to be forgotten, compliance obligations, its link to AI, and FAQs.
What is function calling? Function calling is when a language model understands a natural-language request and produces, as structured JSON, which predefined function to call and with what arguments. This guide: a clear definition, why it is needed, how it works, JSON schema, tool use, API integration, its relation to MCP, security, and FAQs.
What is a digital twin? A digital twin is a virtual replica of a physical asset or process that is continuously updated with real-time sensor data. This guide: a clear definition, how a digital twin works, its link to IoT data, simulation and predictive maintenance, digital twin types, production optimization, examples, limits, and FAQs.
What is a diffusion model? A diffusion model is a generative AI model that starts from pure noise and, by applying step-by-step denoising, produces a meaningful image, audio, or data sample. This guide: a clear definition, how it works, forward and reverse processes, latent space, Stable Diffusion, image generation examples, the difference from GANs, limits, and FAQs.