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Gradient Descent is the core optimization algorithm for solving function minimization. The algorithm determines the direction of descent by calculating the gradient of the function (the vector consisting of the partial derivatives of each), and iteratively updating the parameters according to the rule θ = θ - η - ∇J(θ).

Logistic Regression is a statistical learning method used to solve binary classification problems. The central goal is to predict the probability that a sample belongs to a particular category based on input features. The model maps the linear output to between 0 and 1 by linearly combining the eigenvalues using an S-shaped function...

Regularization is a core technique in machine learning and statistics to prevent model overfitting. Regularization controls the degree of fitting by adding a penalty term to the objective function that is related to the complexity of the model. Common forms include L1 and L2 regularization: the L1 produces sparse solutions and applies...

Generative Adversarial Network (GAN) is a deep learning model proposed by Ian Goodfellow et al. in 2014. The framework implements generative modeling by training two neural networks against each other...

Self-Attention is a key mechanism in deep learning, originally proposed and widely used in the Transformer architecture. The core idea is to allow the model to simultaneously attend to all positions in the input sequence, and compute each position by weighted aggregation of...

Multi-Task Learning (MTL) is not an isolated algorithm, but an intelligent machine learning paradigm.

Diffusion Model (Diffusion Model) is a generative model specialized for creating new data samples such as images, audio or text. The core of the model is inspired by the process of diffusion in physics, which simulates the natural diffusion of particles from a region of high concentration to a region of low concentration. In the machine...

Model fine-tuning (Fine-tuning) is a specific implementation of transfer learning in machine learning. The core process is based on pre-trained models, which utilize large-scale datasets to learn generic patterns and develop extensive feature extraction capabilities. The fine-tuning phase then introduces task-specific datasets to ...

Attention Mechanism (Attention Mechanism) is a computational technique that mimics human cognitive processes, initially applied in the field of machine translation, and later becoming an important part of deep learning.

The Transformer architecture is a deep learning model designed for processing sequence-to-sequence tasks such as machine translation or text summarization. The core innovation is the complete reliance on self-attention mechanisms, eschewing traditional loops or convolutional structures. Allowing the model to process all elements of a sequence in parallel, large...