Artificial Intelligence (AI)
Artificial intelligence (AI) refers to technology or systems that perform tasks by imitating human intelligence. The goal of AI is to enable computers to perform intellectual activities such as learning, reasoning, recognition, and problem solving. There are two types of AI: “narrow AI” (specialized for specific tasks) and “general AI” (capable of handling a wide range of tasks like humans), narrow AI is currently being used in many practical fields. AI technology is based on fields such as machine learning, deep learning, and natural language processing, and it is possible to use these to apply it to a wide range of areas, including image recognition, voice recognition, automated driving, and medical diagnosis. AI is rapidly developing in areas such as business, education, medicine, and entertainment, and it is expected to play an important role in society in the future.
Machine Learning
Machine Learning is a field of AI that uses data to teach computers to learn patterns and perform tasks without explicit programming instructions. The main approaches to machine learning include supervised learning (training with labeled data), unsupervised learning (extracting patterns from unlabeled data), and reinforcement learning (learning based on rewards). Using these techniques, models learn from given data and automatically perform tasks such as prediction, classification, and optimization. Machine learning is used in many fields, including image recognition, speech recognition, recommendation systems, natural language processing, and autonomous driving, and it is an important technology that drives innovation in business and technology because it enables computers to improve through experience.
Deep Learning
Deep Learning is a type of machine learning that uses multi-layer neural networks to automatically learn advanced features from complex data. Deep Learning uses neural networks with structures that mimic the neural circuits of the brain to process data in various formats such as images, audio and text. This technology demonstrates high performance in complex tasks that were difficult to achieve using conventional methods, such as image recognition, voice recognition, and natural language processing, and is being applied to areas such as automated driving, medical diagnosis, and facial recognition systems. Deep learning requires large amounts of data and computing resources to be successful, but due to its powerful expressive power and versatility, it is widely used as a fundamental technology for modern AI.
Neural Network
A neural network is a computational model that mimics the neurons of the human brain, and is a technology that forms the basis of machine learning, particularly deep learning. A neural network is made up of multiple layers, including an input layer, an intermediate layer (hidden layer), and an output layer, and the nodes (neurons) in each layer are connected to each other to transmit information. Depending on the number of hidden layers and the way the neurons are connected, it can be used for a wide range of tasks, from simple problems to very complex pattern recognition. The more layers a network has, the more it is often referred to as deep learning. Neural networks are particularly powerful in fields such as image classification, speech recognition and natural language processing, and they demonstrate excellent performance in pattern recognition, prediction and data classification.
Algorithm
An algorithm is a procedure or calculation method for solving a specific problem. In the fields of computer science and AI, algorithms are the basic building blocks for processing data and performing tasks efficiently. Algorithms are used for a variety of tasks, such as numerical calculations, data retrieval, classification, optimization, and prediction, and they allow computers to automatically solve complex problems. For example, in machine learning, algorithms such as gradient descent are used to train models, automatically extracting patterns and relationships from data. The efficiency and accuracy of algorithms directly affects system performance, so they are constantly being improved. Today, algorithms that are specialized for specific problems, such as evolutionary algorithms and quantum algorithms, are also being developed and used in a variety of fields.
Dataset
A dataset is a collection of data used for training and evaluating machine learning models. Datasets are the source of information for models to learn from, and are usually made up of input data and corresponding labels (output data). Datasets can take many forms, including numerical data, images, text, and audio. Datasets are typically divided into training, validation, and test sets, with training data used to train the model and test data used to evaluate the model’s performance. The quality and quantity of the dataset directly affects the accuracy and generalization ability of the model, so it is important to collect accurate and diverse data and preprocess it appropriately. In the field of machine learning, there are well-known datasets such as MNIST (for handwritten digit recognition) and ImageNet (for image recognition), which are widely used in research and development.
Model
A model is a mathematical structure that expresses patterns and relationships learned from data using machine learning algorithms. Models are used to make predictions and classifications for given input data. In machine learning, models are trained on a dataset, and as a result, they can make appropriate predictions even for unknown data. There are various types of models, such as linear regression, logistic regression, neural networks, and decision trees, and the type of model used is selected according to the nature of the problem and the characteristics of the data. The performance of the model depends on the training data and the algorithm behind it, and sometimes hyperparameter tuning and regularization are required to build the optimal model. Finally, the model is used in the inference phase once training is complete, and it is actually applied to real-world problems.
Training
Training refers to the process of a machine learning model learning from a dataset and improving its performance. During training, the model uses the inputs and corresponding outputs (labels) in the dataset to adjust its parameters in a way that minimizes the prediction error. This is done using a loss function and an optimization algorithm, which updates the parameters in a way that reduces the error, for example, using a gradient descent method. Training is carried out by repeating epochs (a process of passing the entire dataset through the model once), and is continued until the model has been sufficiently trained. However, overfitting (over-learning) may occur if training is carried out too much, so a moderate number of epochs and regularization are necessary. As a result of training, the model is expected to have high prediction accuracy even for new data.
Inference
Inference refers to the process of a trained machine learning model making predictions or judgments on new data. Once training is complete, the model will apply the learned patterns and rules to unknown data and perform tasks such as classification and regression. For example, in an image recognition model, when a new image is given as input, the model will predict the content of the image and return a label. Inference plays an important role when the model is used in a real-world environment, and is used for real-time prediction and decision-making. The efficiency of inference is particularly important when dealing with large amounts of data or in environments with limited computing resources. In order to achieve fast and accurate inference, it is necessary to optimize and reduce the size of the model, and it may be adjusted to work effectively on mobile devices and edge computing.
Supervised Learning
Supervised Learning is a method of machine learning that trains a model using labeled data. The training data set contains input data and the corresponding correct labels, and the model learns the relationship between input and output based on these pairs. The goal of supervised learning is to optimize the model so that it can make accurate predictions and classifications for new data. Typical algorithms include linear regression, logistic regression, support vector machines (SVM), and decision trees, which are applied to regression and classification problems. Supervised learning is used in a wide range of fields, including medical diagnosis, image recognition, speech recognition, and spam filtering, and is most effective when there is a dataset with clear labels. In addition, it is necessary to devise a way to prevent overfitting, using test data that differs from the training data to evaluate the performance of the model.
Unsupervised Learning
Unsupervised learning is a method of machine learning that uses unlabeled data to automatically learn the internal structure and patterns of the data. In unsupervised learning, there are no correct labels for the data points, so the model extracts features and patterns from the data itself and performs classification and clustering. Typical methods include clustering (K-means, hierarchical clustering) and dimensionality reduction (principal component analysis (PCA), singular value decomposition (SVD)). Clustering groups together data points that are similar, while dimensionality reduction expresses the important features of the data in fewer dimensions. Unsupervised learning is effective when there is little prior knowledge of the data or when the cost of labeling is high. Some applications include customer segmentation, anomaly detection, and some functions of recommendation systems, and it is useful for exploring data and discovering new knowledge.
Semi-Supervised Learning
Semi-Supervised Learning is a method of training a model using a combination of a small amount of labeled data and a large amount of unlabeled data. This approach is particularly effective when the cost of collecting labeled data is high or when labeling is difficult. The model learns basic patterns from the labeled data, and then uses the unlabeled data to further strengthen those patterns. This is expected to achieve high performance with less labeled data than with fully supervised learning. Semi-supervised learning is often used in fields such as medical image analysis and natural language processing, and it supplements the lack of labeled data while extracting important features from vast amounts of data. In general, it aims to make effective use of data and improve performance as an intermediate approach between supervised learning and unsupervised learning.
Reinforcement Learning
Reinforcement Learning is a method of machine learning in which an agent learns through interaction with its environment. The agent chooses an action based on its current state, and then learns based on the resulting reward or punishment. The goal is to learn the optimal action strategy (policy) to maximize cumulative reward in the future. Reinforcement learning is effective in situations where clear teacher data is not provided, and it finds the best action through trial and error. Examples of its application include self-driving cars, game AI, and robot control. Reinforcement learning algorithms include Q-learning and policy gradient methods, and the agent learns while balancing exploration and exploitation. A feature of reinforcement learning is that the results of actions often do not appear immediately, and it takes into account long-term rewards.
Transfer Learning
Transfer Learning is a machine learning method that applies knowledge or models learned in one task to another related task. Usually, machine learning models are optimized for a specific task, but in transfer learning, existing learned models or some of their parameters can be reused and applied to new tasks, greatly reducing training time and the amount of data. For example, it is possible to use a network trained on an image recognition model and apply it to a different type of image classification task. Transfer learning is effectively used in fields where data is limited, such as medical imaging and speech recognition. It is also possible to use a model pre-trained on a large dataset (e.g. a model trained on ImageNet) to achieve high accuracy on a task with a small amount of data.
Meta Learning
Meta learning is a method that aims to “learn how to learn”, and it enables models to quickly adapt to small amounts of data and new tasks. While conventional machine learning requires large amounts of data, meta learning efficiently adapts to new tasks by utilizing the knowledge gained from multiple tasks learned in the past. Meta learning is particularly effective when there is a need to respond quickly to new environments and data. For example, when a robot learns a new operation, it can use the experience of movements it has already learned to acquire the new operation in a small number of trials. Meta-learning is attracting attention as an approach to making artificial intelligence more like the human learning process, and it aims to build a system with general-purpose learning ability for various tasks and environments as a “model of a model”.
Online Learning
Online Learning is a machine learning method that learns by continuously updating a model in an environment where data is provided sequentially. With Online Learning, the model is updated each time a new data point arrives, allowing predictions to be made based on the latest data at all times. This enables learning to be carried out while keeping memory usage down compared to batch learning, which processes large amounts of data at once. In addition, the model adapts to non-stationary environments where the distribution of data changes over time, making it suitable for real-time applications and processing streaming data. For example, online learning is used in stock price prediction and online advertising targeting, where new information is constantly flowing in. This method enables efficient model updating in situations where data is generated sequentially and computational resources are limited.
Batch Learning
Batch learning is a method of training a model using all the training data at once. In this approach, the entire dataset is used to train the model in one go, so once training is complete, the model will not be updated even if new data comes in, and will only make predictions. Batch learning is effective when the data is static and infrequently updated, or in environments where large amounts of data can be processed at once. However, if the data set is large, batch learning requires a lot of computing resources and time, and memory constraints can also be a problem. Batch learning is not suitable for real-time data updates, although it does maintain a high level of model accuracy because it processes data in batches after it has been accumulated. Typical applications include large-scale models for image recognition and speech recognition.
Epoch
An epoch is a unit of processing in the machine learning training process, in which the model learns the entire dataset once. In one epoch, all the samples in the training data are passed through the model once, and the parameters are updated. Usually, a single epoch is not sufficient to train the model, so the training process is repeated multiple times. For example, if the entire dataset has 1000 samples, then after one epoch, the model parameters are updated once based on all the samples. The number of epochs is an important hyperparameter for preventing overfitting and underfitting, and choosing the right value will improve the model’s performance. Generally speaking, if the number of epochs is too small, the model will not learn sufficiently, and if the number of epochs is too large, there is a risk of overfitting.
Batch Size
The batch size refers to the number of samples of training data processed at once in machine learning training. Usually, it is difficult to process the entire large dataset at once due to memory and computational resource constraints, so the data is divided into several small batches for training. Each batch is processed before updating the model parameters, and a larger batch size allows more accurate gradient estimation, but increases memory consumption. On the other hand, a smaller batch size may cause unstable learning, but improves memory efficiency. The choice of batch size is one of the most important hyperparameters, as it has a significant impact on training efficiency and accuracy. Small batch sizes are part of the stochastic gradient descent (SGD) method, while large batch sizes are used as part of the mini-batch gradient descent method.
Hyperparameters
Hyperparameters are parameters that are set in advance to control the learning process of a machine learning model, and they directly affect the behavior of the learning algorithm without being dependent on the training data. In contrast, internal parameters (e.g., weights and biases in neural networks) that are adjusted by learning the model are automatically optimized based on the training data. Typical hyperparameters include the learning rate, number of epochs, batch size, regularization parameter, depth of decision tree, etc., and these values have a significant impact on the performance and training speed of the model. Hyperparameter tuning is performed using methods such as grid search, random search, and Bayesian optimization, and it is important to find the optimal combination. Choosing the right hyperparameters can prevent overfitting and underfitting of the model, and lead to an improvement in overall performance.
Parameter
Parameters are values that are adjusted by machine learning models through training, and are numerical elements that capture patterns and relationships from data. These parameters are optimized based on training data and play an important role in making predictions and classifications for new data. For example, in linear regression, the weights (coefficients) and bias (intercept) are adjusted as parameters, and in neural networks, the weights and bias of each layer correspond to parameters. The performance of the model depends on the appropriate setting of these parameters, and the aim is to find the optimal values through the training process. By adjusting the parameters appropriately, the model will be able to more accurately capture the characteristics of the data and make highly accurate predictions for unknown data.
Loss Function
The loss function is a function used to quantitatively measure the difference between the model’s prediction and the actual value. In machine learning, it is used to evaluate the model’s performance and find the optimal parameters. The loss function calculates the difference between the predicted value and the correct value, and the goal of training is to minimize this error. For example, mean squared error (MSE) is commonly used for regression problems, and cross-entropy loss is commonly used for classification problems. The choice of loss function has a significant impact on the efficiency of model training and the final performance, so it is important to choose the appropriate one for the type of problem. The loss function also serves as a metric for the optimization algorithm to calculate gradients and update parameters, and plays an essential role in improving the model.
Objective Function
The objective function is the function that is to be minimized or maximized in machine learning and optimization problems. In machine learning, the objective function is usually expressed as a loss function, and the goal is for the model to minimize the error through the learning process. For example, the objective function of linear regression is to minimize the sum of the squared errors between the predicted value and the actual value. The objective function acts as a criterion for adjusting the model parameters, and the value is minimized or maximized using an optimization algorithm (e.g. gradient descent). The selection of the objective function has a significant impact on the learning efficiency and accuracy of the model, so it is important to choose the most appropriate one for the problem. In addition, if regularization is included, it is also possible to prevent overfitting by adding a penalty term to the objective function.
Gradient
The gradient is a vector that indicates the rate of change of a function, and it plays an important role, especially in the optimization process of machine learning. In order to adjust the parameters of the model, the gradient of the loss function is calculated, and the parameters are updated in the direction of the gradient to minimize the loss. For example, in gradient descent, the parameters are gradually adjusted in the direction of the gradient so that they move towards the minimum value of the loss function. The gradient indicates the slope of the function, so if you move in the direction of a large gradient, the loss function will change rapidly, and if you move in the direction of a small gradient, it will change slowly. The gradient is calculated by back-propagation, which is particularly important in deep learning, to enable the model to learn efficiently.
Optimization
Optimization refers to the process of adjusting the parameters of a model to minimize or maximize the objective function (usually the loss function). The goal of optimization in machine learning is to ensure that the model makes the most appropriate predictions for the training data. Optimization algorithms include Gradient Descent, Stochastic Gradient Descent (SGD), and Adam, which update parameters using the gradient of the objective function. Since optimization directly affects the performance of the model, it is important to choose an efficient algorithm and appropriate hyperparameters. With optimization algorithms such as gradient descent, if the learning rate is not appropriate, the model may not converge to the optimal solution or may be excessively modified. Optimization is an essential step for machine learning models to make accurate predictions.
Overfitting
Overfitting is a phenomenon in which a machine learning model becomes too adapted to the training data, and is unable to make accurate predictions for new data. This occurs because the model responds to the noise and minute features of the training data, resulting in an overly complex model. A model that has overfitted will show very high accuracy for the training data, but its generalization ability will be reduced for unknown data, and it will tend to make incorrect predictions. To prevent this problem, measures such as dividing the data set and evaluating it using test data and validation data, applying regularization (L1 or L2 regularization), and using techniques such as dropout are taken. Overfitting is a problem that tends to occur frequently, especially with complex models (such as deep learning and decision trees).
Underfitting
Underfitting refers to a situation where the model is unable to adequately capture the characteristics and patterns of the training data, and is therefore unable to make appropriate predictions for the data. This occurs when the model is too simple and is unable to fit either the training data or the test data. Models that underfit will show low accuracy for both the training data and the test data. The causes include insufficient model structure, a small number of training iterations (epochs), and insufficient feature values. To prevent underfitting, it is important to increase the complexity of the model, perform appropriate feature value engineering, and provide sufficient training. Underfitting reduces prediction performance because the model is unable to capture the patterns in the data.
Regularization
Regularization is a technique for preventing overfitting in machine learning models, and aims to limit the complexity of the model and improve generalization performance. The main methods of regularization are L1 regularization (Lasso) and L2 regularization (Ridge), which suppress overfitting by imposing a penalty on the model parameters . L1 regularization has the effect of setting some parameters to zero, and also has the effect of feature selection. L2 regularization has the effect of gradually reducing all parameters, and improves the stability of the model. In neural networks, dropout (random deletion of nodes) is also a regularization method. Regularization is important for balancing the bias and variance of the model, and by setting the parameters appropriately, it is possible to prevent overfitting and maintain high performance even on test data.
Generalization
Generalization refers to the ability of a machine learning model to make accurate predictions on unknown data without being overly adapted to the training data. A model with high generalization ability can maintain high accuracy on test data and new data sets without being specialized to the training data. If the generalization ability is low, overfitting or underfitting will occur, and the model will not be able to demonstrate the expected performance in the actual environment. In order to improve the generalization ability of the model, it is important to ensure an appropriate data set division, cross-validation, the introduction of regularization methods, and sufficient data volume. In addition, designing a model that can respond even if the distribution of the data changes is also a strategy for improving generalization ability. Models with high generalization ability will also demonstrate stable prediction accuracy for new data.
Bias
Bias is a type of error that occurs when a machine learning model makes a prediction. It refers to a situation where the model is too simple and does not fully capture the essence of the data, resulting in a consistent discrepancy between the predicted value and the actual value. A model with high bias shows low accuracy for both training data and test data, indicating that underfitting has occurred. For example, if a linear regression model is applied to complex non-linear data, the bias will be high and the model will not be able to properly capture the pattern of the data. Bias is part of a concept known as the bias-variance trade-off, where the simpler the model, the higher the bias, but at the same time the lower the variance. To keep the bias low, you need to either choose a more complex model or devise more features.
Variance
Variance is a measure of how sensitive a model is to small variations or noise in the training data in machine learning. A model with high variance may overfit the training data and respond to small variations in the data, which can lead to overfitting. This means that while the model may show high accuracy for the training data, its accuracy for new data will be significantly lower. When the variance is low, the model will perform well both on the training data and the test data, but on the other hand, it may not be able to capture complex patterns in the data, and this can lead to underfitting. There is a “bias-variance trade-off” between bias and variance, and it is important to strike a balance between the two when selecting and tuning models. In order to maintain appropriate variance, it is effective to adjust the diversity of data and the complexity of the model, and to incorporate regularization methods.
Feedback Loop
A feedback loop is a situation in which the output of a system affects its future input. In machine learning and AI, a feedback loop occurs when the results output by a prediction or recommendation system are then taken back into the system as new data. For example, in a recommendation system, when a user selects a product or piece of content that is presented to them, that selection data is fed back into the system and affects the next recommendation. Such feedback loops can increase the risk of the system reinforcing biased recommendations or predictions. For example, if a particular item is frequently recommended, the data about that item will increase, creating a self-reinforcing cycle in which that item is more likely to be recommended. This can encourage bias and affect the diversity and fairness of the system. In order to manage feedback loops appropriately, it is important to design algorithms that avoid data bias and maintain diversity, as well as to have external oversight.
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