After the emergence of large models, a plethora of artificial intelligence-related concepts such as models, algorithms, and training have come into our view.
Algorithm: A clear sequence of steps to perform a specific task.
Model: A mathematical representation or simulation of the real world, referring to a specific outcome.
Data Model: An abstract structure that describes data objects, their relationships, and associated operations.
Model Structure: The specific framework or architecture of a model.
Training: The process of adjusting model parameters using data to improve model performance.
Algorithm
1. Definition
Algorithm: A clear, ordered, and finite set of steps used to solve a specific problem or perform a specific task, applicable to both simple everyday tasks and complex computer science problems.
(1) Clear: Each step of the algorithm should be clear and unambiguous, understandable, and executable by anyone following its instructions.
(2) Ordered: The sequence of steps is fixed, ensuring that the same result is produced each time the algorithm is run (assuming initial conditions and inputs remain unchanged).
(3) Finite: The execution ends after a certain number of steps and does not continue indefinitely.
(4) Solve Problems or Perform Tasks: The goal is to solve a specific problem or perform a specific task, whether calculating numbers, sorting lists, or other more complex tasks.
2. Machine Learning Algorithms
In the field of artificial intelligence, algorithms typically refer to methods that learn to automatically improve performance or gradually adapt to a task through data or experience, which distinguishes them from traditional algorithm definitions.
(1) Learning from Data or Experience: Machine learning algorithms usually require datasets for training, which allow the algorithms to recognize patterns, make predictions, or perform other tasks.
(2) Automatically Improve Performance: Over time and with more data input, machine learning algorithms aim to enhance the quality of task completion, whether it be classification accuracy, prediction precision, or other metrics.
(3) Gradually Adapt to a Task: This highlights the learning ability of machine learning algorithms, meaning they become increasingly proficient at specific tasks over time.
Artificial intelligence algorithms include decision tree algorithms, neural network algorithms, genetic algorithms, etc., each with its specific learning methods and applicable task types.
To implement their algorithms faster and better, people often prefer to encapsulate already implemented and effective algorithms into artificial intelligence frameworks; TensorFlow, PyTorch, and MindSpore are commonly used AI frameworks.
The currently popular Transformer in large models can be considered an algorithm because it describes how to perform self-attention calculations, how to combine input data, and how to pass data through neural network layers, etc. In this sense, the algorithm describes the computational steps taken by the model during forward and backward propagation.
Model
1. Definition
Model: A simplified and abstract representation of a part of the real world, used to simulate, describe, predict, or understand the behavior or phenomena of that part.
(1) Simplification and Abstraction: Since it is impossible or difficult to fully simulate reality, a model simplifies reality, only including parts relevant to specific purposes while ignoring unrelated or secondary details.
(2) Representing a Part of the Real World: This can be a physical system, economic process, biological entity, or any other observable and describable entity.
(3) Simulate, Describe, Predict, or Understand: The purposes of a model can vary. Some models are used to simulate real-world behavior (e.g., flight simulators), others may be used for predictions (e.g., weather models), and some models are for theoretical research and understanding fundamental principles.
2. Data Model
A data model, like a general model, is a simplified and abstract representation of real-world entities, but it embodies the projection of real-world or business logic at the data level, organizing data elements in a standardized manner to simulate the information framework and blueprint of the real world.
For instance, through abstraction, a data model can provide a more understandable and operable view of the system interactions in the world, focusing on entities and relationships critical to specific tasks or goals while ignoring irrelevant or unimportant details.
A data model is an abstract description and organization of data, data relationships, data semantics, and data constraints. It provides a framework for structuring data and determines how data is stored, organized, and processed.
A data model can help ensure data integrity, accuracy, and availability. Data models can be categorized into different levels or types, including conceptual data models, conceptual logical models, and physical data models.
3. Machine Learning Model Definition
A machine learning model is also a description and abstraction of a certain phenomenon or data. Unlike general models based on a theory, principle, or experience, machine learning models derive a mathematical structure through data training, aiming to capture and represent patterns or relationships between data to make predictions or decisions on new, unseen data. The key points are:
(1) Obtained through Data Training: In machine learning, models are typically not manually created or programmed for specific tasks but rather automatically “learn” or adjust their parameters using data and specific learning algorithms.
(2) Mathematical Structure: At its core, the model is mathematical, whether it be linear equations in linear regression, weights and biases in neural networks, or node decisions in decision trees.
(3) Capture and Represent Patterns or Relationships between Data: The primary task of the model is to understand potential patterns in the data and be able to make decisions or predictions based on these patterns.
(4) Make Predictions or Decisions on New, Unseen Data: A good machine learning model should not only handle the data it saw during training well but also generalize to new data it has not encountered during training.
Model Structure
In machine learning and deep learning, model structure typically refers to the design or framework of the model, defining the core components of the model and how they are interconnected. The model structure includes elements such as layers, nodes, weights, and connections, along with their layout and organization.
Here are some further explanations of model structure:
1. Definition of Layers: For example, in a neural network, the model structure defines how many layers there are and how many nodes each layer has. For convolutional neural networks, the model structure also defines the number and order of convolutional layers, pooling layers, and fully connected layers.
2. Connection Methods: The model structure defines how various nodes or layers are connected, such as fully connected, locally connected, or skip connections.
3. Parameters: The model structure also defines the parameters of the model. For example, in a neural network, this includes the weights of each connection and the biases of each node.
4. Activation Functions: In model structures, each node or certain layers may have activation functions, such as ReLU, sigmoid, or tanh.
5. Other Components for Complex Models: For example, in Long Short-Term Memory networks (LSTM), the model structure involves more components, such as gating units.
The model structure provides an overview of the model and serves as a framework for training and applying the model. Once the model structure is defined, data and algorithms can be used to “train” the model, finding the optimal values for the model parameters. Here are some commonly used model structures:
1. Linear Models
Linear Regression
Logistic Regression
2. Instance-Based Models
K-Nearest Neighbors (K-NN)
3. Decision Tree Models
Decision Trees
Random Forests
Gradient Boosting Trees
4. Support Vector Machines
Linear Support Vector Machines (SVM)
Non-linear SVM
5. Ensemble Methods
Bagging
Boosting (e.g., AdaBoost, GBM, XGBoost, LightGBM)
6. Neural Networks and Variants
Multilayer Perceptrons (MLP)
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Long Short-Term Memory (LSTM)
Transformer Networks
GANs (Generative Adversarial Networks)
7. Bayesian Models
Naive Bayes
Gaussian Processes
8. Clustering Models
K-means
Gaussian Mixture Model (GMM)
DBSCAN
9. Dimensionality Reduction Methods
Principal Component Analysis (PCA)
t-SNE (t-distributed Stochastic Neighbor Embedding)
10. Others
Reinforcement Learning Models
Time Series Models (e.g., ARIMA, Prophet)
Association Rule Learning Models (e.g., Apriori, FP-Growth)
These model structures have different application domains and advantages. For example, CNNs are commonly used for image processing tasks, RNNs and LSTMs are often applied to sequential data such as time series and natural language processing tasks, while decision trees and their ensemble versions (like random forests) perform well in many classification and regression tasks.
The choice of model structure typically depends on the nature of the problem (e.g., whether it is classification, regression, clustering, or another type of problem), the type of data (e.g., whether it is tabular data, images, text, or sequential data), and the project requirements (e.g., interpretability, real-time performance, accuracy, etc.).
Training (Learning)
In machine learning, training is a core concept, referring to the process of using data to “teach” a machine learning model so that it can make predictions or decisions for specific tasks. Here is a detailed explanation of training:
1. Goal: The main goal of training is to adjust the model’s parameters so that it can accurately represent or fit the training data and make effective predictions on new, unseen data.
2. Process: Initially, the model is usually untrained, with random or preset parameter values. During training, the model repeatedly examines the training data and attempts to adjust its parameters to reduce the error between its predictions and the actual labels.
An optimization algorithm (such as gradient descent) is used to guide how to update the parameters.
3. Error/Loss: To quantify the difference between the model’s predictions and actual values, a loss function is typically used. The goal of training is to minimize this loss function.
4. Iteration: Training usually involves multiple iterations, with each iteration fine-tuning the model’s parameters to reduce errors.
5. Overfitting and Regularization: While the model may perform well on the training data, it may sometimes be overly complex, leading to poor performance on new data. This phenomenon is known as overfitting. Various regularization techniques can be used to avoid overfitting.
In short, training is about using data to “teach” the model to make meaningful predictions based on input. This is typically achieved by adjusting the model’s parameters until its performance on the training data reaches a satisfactory level.
Source: Excerpts from the WeChat account “Data Companion” articles