18 posts
What is cross entropy? Cross entropy is a loss function that measures the difference between the probability distribution a model predicts and the true label. This guide: a clear definition, its relation to entropy, its link with softmax, binary and multi-class cross entropy, why it is the standard classification loss, real-world examples, and FAQs.
What is SMOTE? SMOTE (Synthetic Minority Over-sampling Technique) is an over-sampling method that fixes class imbalance by generating new, synthetic examples of the minority class in imbalanced datasets. This guide: a clear definition, why SMOTE is needed, how it works, its variants, real-world and Türkiye examples, its limits, and FAQs.
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 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 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 anomaly detection? Anomaly detection is the process of automatically finding observations (outliers) in a dataset that deviate markedly from the expected pattern. This guide: a clear definition, how it works, methods like isolation forest, fraud detection and time series anomaly examples, supervised/unsupervised approaches, common mistakes, and FAQs.
What is logistic regression? Logistic regression is a statistical machine learning model used for binary classification that estimates the probability of an observation belonging to a given class. This guide: a clear definition, the sigmoid function, odds ratio, difference from linear regression, how it works, industry examples, multiclass variants, common mistakes, and FAQs.
What is reinforcement learning? Reinforcement learning is a machine learning approach where an agent learns the best behavior through trial and error inside an environment, based on the rewards and penalties it receives. This guide: a clear definition, agent and environment, the reward function, how it works, Q-learning, deep RL, RLHF, real-world examples, the difference from supervised learning, limits, and FAQs.
What is a neural network? A neural network is a machine learning model inspired by the neurons in the human brain, learning patterns from data through connected layers of artificial neurons. This guide: a clear definition, neuron and layer structure, activation functions, learning via backpropagation, types of neural networks, real-world examples, its relation to deep learning, and FAQs.
What is data mining? Data mining is the discovery of previously unknown, useful patterns and relationships in large volumes of data using statistics and machine learning. This guide: a clear definition, pattern discovery, clustering and classification, association rules, the CRISP-DM process, real-world examples, KVKK/GDPR, and FAQs.
What is data science? Data science is the discipline that turns raw data into knowledge and predictions to guide business decisions, using statistics, programming, and machine learning. This guide: a clear definition, why it matters, how the data analysis process works, what a data scientist does, the role of machine learning and Python, data science vs data analytics, Türkiye examples, KVKK, and FAQs.
What is MLOps? MLOps (Machine Learning Operations) is the engineering discipline that ties developing, deploying, monitoring, and updating machine learning models into a single repeatable process. This guide: a clear definition, why MLOps is needed, how it works, model deployment, CI/CD, model monitoring, MLOps vs DevOps, the LLMOps difference, GDPR, and FAQs.
What is machine learning? Machine learning is the branch of AI that lets a computer produce predictions or decisions by learning patterns from data, rather than being explicitly programmed with rules. This guide: a clear definition, how machine learning works, supervised and unsupervised learning, model training, examples, use cases, limits and FAQs.
What is deep learning? Deep learning is more than simply using neural networks with many layers. It is a way of learning representations from data, capturing patterns at multiple levels of abstraction, and optimizing complex decision systems end to end. This is why it has become central in computer
What is artificial intelligence? Artificial intelligence (AI) is the set of disciplines that enable machines to imitate human-like learning, reasoning, perception, and decision-making. This guide is a 2026 reference covering AI's definition, types, core technologies, industry applications, and Turkey-specific regulatory context.
Enterprise AI architecture is not just about selecting a large language model. A reliable AI system requires data pipelines, model infrastructure, API integrations, security controls, observability, workflow orchestration, human approval mechanisms and governance layers. This guide explains how to design production-ready enterprise AI systems from a strategic and technical perspective.
Three of the most commonly confused concepts in deep learning are transfer learning, fine-tuning, and representation learning. They are not the same thing, but they are tightly connected. Representation learning refers to learning useful and generalizable internal features from data. Transfer learning is the broader strategy of reusing knowledge learned in one task or domain for another task or domain. Fine-tuning is often the practical adaptation mechanism used to realize that transfer. Put differently, strong representations make transfer possible, transfer learning defines the reuse logic, and fine-tuning operationalizes it. This guide explains the historical development, conceptual relationship, practical differences, and enterprise relevance of these three ideas in modern AI systems.