All public logs

Combined display of all available logs of AI Wiki. You can narrow down the view by selecting a log type, the username (case-sensitive), or the affected page (also case-sensitive).

22:28, 21 March 2023 Walle talk contribs created page Unawareness (to a sensitive attribute) (Created page with "{{see also|Machine learning terms}} ==Unawareness in Machine Learning== Unawareness in machine learning refers to the deliberate exclusion or ignorance of specific sensitive attributes during the process of model training and decision-making. Sensitive attributes are those that may potentially lead to unfair or discriminatory outcomes, such as race, gender, age, or sexual orientation. The primary goal of incorporating unawareness in machine learning is to ensure fairness...")
22:28, 21 March 2023 Walle talk contribs created page Transfer learning (Created page with "{{see also|Machine learning terms}} ==Introduction== Transfer learning is a subfield of machine learning that focuses on leveraging the knowledge gained from solving one problem and applying it to a different but related problem. The primary motivation behind transfer learning is to reduce the amount of time, computational resources, and data required to train models for new tasks by reusing the knowledge gained from previous tasks. In this article, we will discuss t...")
22:28, 21 March 2023 Walle talk contribs created page Tower (Created page with "{{see also|Machine learning terms}} ==Tower in Machine Learning== The term "tower" in machine learning typically refers to a specific arrangement of layers within a neural network architecture. The term is primarily used to describe architectures where multiple parallel branches are vertically stacked, allowing for a hierarchical structure that can help improve the model's performance and accuracy. ===Background=== Tower architectures were introduced as a way to address...")
22:28, 21 March 2023 Walle talk contribs created page Tf.keras (Created page with "{{see also|Machine learning terms}} ==Introduction== '''tf.keras''' is a high-level neural networks API, integrated within the TensorFlow machine learning framework. Developed by the Google Brain Team, tf.keras is designed to facilitate the creation, training, and evaluation of deep learning models. It is designed for quick prototyping and is user-friendly, modular, and extensible. In this article, we explore the key features and components of tf.keras, its advantage...")
22:28, 21 March 2023 Walle talk contribs created page Tf.Example (Created page with "{{see also|Machine learning terms}} ==Introduction== In the realm of machine learning, '''''tf.Example''''' is a standard data serialization format employed by the TensorFlow framework, which is an open-source library developed by the Google Brain Team. The primary purpose of ''tf.Example'' is to facilitate the storage and exchange of data across diverse machine learning pipelines. This data structure efficiently represents data as a collection of key-value pairs, ma...")
22:28, 21 March 2023 Walle talk contribs created page Test set (Created page with "{{see also|Machine learning terms}} ==Test Set in Machine Learning== ===Definition=== In the context of machine learning, the '''test set''' refers to a subset of data that is distinct from the data used for model training and validation. It is typically utilized to evaluate the performance and generalization capabilities of a machine learning model after the training and validation processes are complete. Test sets play a vital role in ensuring that a model can perf...")
22:27, 21 March 2023 Walle talk contribs created page Temporal data (Created page with "{{see also|Machine learning terms}} ==Temporal Data in Machine Learning== Temporal data, also known as time series data, refers to data containing time-dependent observations. These data points are collected at consistent time intervals, which can range from milliseconds to years. In the context of machine learning, temporal data is used to build models that can analyze and predict trends, patterns, and relationships over time. Time series analysis and forecasting are wi...")
22:27, 21 March 2023 Walle talk contribs created page Target (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, the term '''target''' refers to the variable or outcome that a learning algorithm aims to predict, estimate, or classify. The target is also commonly referred to as a '''label''' or '''ground truth'''. Machine learning models utilize target data during the training phase to learn patterns, relationships, or rules, and subsequently generalize these findings to make predictions on un...")
22:27, 21 March 2023 Walle talk contribs created page Summary (Created page with "{{see also|Machine learning terms}} ==Summary in Machine Learning== In machine learning, a '''summary''' refers to the process of reducing a large dataset or model into a simplified representation, which retains the most essential information. This can be done through various methods, such as dimensionality reduction, model compression, and ensemble methods. Summarization is crucial for improving computational efficiency, enhancing interpretability, and mitigating overfi...")
22:27, 21 March 2023 Walle talk contribs created page Structural risk minimization (SRM) (Created page with "{{see also|Machine learning terms}} ==Introduction== Structural Risk Minimization (SRM) is a fundamental concept in the field of machine learning and statistical learning theory, introduced by Vladimir Vapnik and Alexey Chervonenkis. It serves as a regularization principle that aims to minimize the risk of overfitting in a model by finding an optimal balance between the model's complexity and its ability to generalize to unseen data. In essence, SRM strives to st...")
22:27, 21 March 2023 Walle talk contribs created page Step size (Created page with "{{see also|Machine learning terms}} ==Definition== In machine learning, the '''step size''' (also known as learning rate or alpha) is a hyperparameter that determines the magnitude of the update applied to the weights of a model during optimization. Step size is a crucial factor in the training process, as it influences the model's convergence speed and its ability to reach the global minimum of the loss function. The step size is used in various optimization algorit...")
22:27, 21 March 2023 Walle talk contribs created page Step (Created page with "{{see also|Machine learning terms}} ==Definition of Step in Machine Learning== In the context of machine learning, a '''step''' typically refers to an iteration or a single pass through a specific part of the algorithm during the learning process. A step can involve various actions, such as updating model parameters, assessing the current model's performance, or executing a certain phase of the algorithm. Steps are often part of larger processes like training, validation...")
22:27, 21 March 2023 Walle talk contribs created page Squared hinge loss (Created page with "{{see also|Machine learning terms}} ==Squared Hinge Loss== Squared hinge loss, also known as the squared variant of the hinge loss, is a popular loss function in the field of machine learning and support vector machines (SVM). It is a modification of the standard hinge loss function that provides better convergence properties and smoothness, while still maintaining the ability to handle non-linear classification problems. The squared hinge loss function can be us...")
22:26, 21 March 2023 Walle talk contribs created page Sparsity (Created page with "{{see also|Machine learning terms}} ==Introduction== Sparsity, in the context of machine learning, refers to the phenomenon where only a small number of features or parameters have significant non-zero values in a model or dataset. This characteristic can be exploited to improve the efficiency and interpretability of machine learning models. The concept of sparsity has been applied in various areas, including feature selection, regularization, and sparse representati...")
22:26, 21 March 2023 Walle talk contribs created page Shape (Tensor) (Created page with "{{see also|Machine learning terms}} ==Definition== A '''shape''' in the context of machine learning and deep learning refers to the structure or dimensionality of a '''tensor''', which is a multi-dimensional array of numerical values. Tensors are the fundamental building blocks of many machine learning models and frameworks, such as TensorFlow and PyTorch. The shape of a tensor is characterized by the number of dimensions it has, known as its '''rank''', and the...")
22:26, 21 March 2023 Walle talk contribs created page Serving (Created page with "{{see also|Machine learning terms}} ==Serving in Machine Learning== Serving in machine learning refers to the process of deploying and utilizing a trained machine learning model to make predictions or decisions based on new input data. This process is an integral part of the machine learning pipeline, as it allows the machine learning models to be applied to real-world problems and provide value to users. The serving process typically follows the completion of the ...")
22:26, 21 March 2023 Walle talk contribs created page Sensitive attribute (Created page with "{{see also|Machine learning terms}} ==Sensitive Attribute in Machine Learning== Sensitive attributes, also known as protected attributes, are variables that carry the potential of causing unfair or biased outcomes in a machine learning algorithm. These attributes often relate to demographic information such as race, gender, age, religion, or disability, and may inadvertently contribute to discriminatory decisions or predictions when used inappropriate...")
22:26, 21 March 2023 Walle talk contribs created page Semi-supervised learning (Created page with "{{see also|Machine learning terms}} ==Introduction== Semi-supervised learning is a type of machine learning approach that combines elements of both supervised and unsupervised learning methods. It leverages a small amount of labeled data along with a larger volume of unlabeled data to train models. This article will provide an overview of semi-supervised learning, discuss its advantages and challenges, and present commonly used techniques. ==Motivation and Advantage...")
22:26, 21 March 2023 Walle talk contribs created page Self-training (Created page with "{{see also|Machine learning terms}} ==Introduction== Self-training, a form of semi-supervised learning, is an approach in machine learning that combines both labeled and unlabeled data to improve the performance of a model. In this method, an initial model is trained on a small set of labeled data, and then it iteratively refines itself by incorporating the predictions it generates for the unlabeled data. This article will discuss the key concepts, advantages, and ch...")
22:25, 21 March 2023 Walle talk contribs created page Weighted Alternating Least Squares (WALS) (Created page with "{{see also|Machine learning terms}} ==Weighted Alternating Least Squares (WALS)== Weighted Alternating Least Squares (WALS) is a widely-used optimization algorithm employed in the field of machine learning. It is particularly popular for addressing the matrix factorization problem, which is often used in collaborative filtering and recommendation systems. WALS iteratively refines the latent factors of the input data to minimize the error, while simultaneously applyin...")
22:25, 21 March 2023 Walle talk contribs created page Wasserstein loss (Created page with "{{see also|Machine learning terms}} ==Wasserstein Loss in Machine Learning== Wasserstein loss, also known as the Earth Mover's Distance (EMD), is a metric used in the field of machine learning, particularly in the training of Generative Adversarial Networks (GANs). Introduced by Martin Arjovsky, Soumith Chintala, and Léon Bottou in their 2017 paper "Wasserstein GAN," this loss function has become a popular choice for training GANs due to its stability and th...")
22:25, 21 March 2023 Walle talk contribs created page Tensor size (Created page with "{{see also|Machine learning terms}} ==Definition== In machine learning, '''tensor size''' refers to the dimensions of a tensor, which is a multi-dimensional data structure often used to represent and manipulate data in various mathematical operations. Tensors are the generalization of scalars, vectors, and matrices, with scalars being zero-dimensional tensors, vectors being one-dimensional tensors, and matrices being two-dimensional tensors. Tensor size, also known a...")
22:25, 21 March 2023 Walle talk contribs created page Tensor shape (Created page with "{{see also|Machine learning terms}} ==Tensor Shape in Machine Learning== Tensor shape is a fundamental concept in the field of machine learning, particularly in deep learning architectures, where tensors are used as the primary data structure for representing and processing multidimensional data. In this article, we will explore the meaning of tensor shape, its significance in machine learning, and some common operations performed on tensors. ===Definition and Backgroun...")
22:25, 21 March 2023 Walle talk contribs created page Tensor rank (Created page with "{{see also|Machine learning terms}} ==Definition of Tensor Rank== In the field of machine learning, tensors are multi-dimensional arrays that provide a mathematical framework to represent and manipulate data. The rank of a tensor, also known as its ''order'', refers to the number of dimensions or indices required to describe the tensor. Formally, the tensor rank is defined as the number of axes within a tensor. In other words, the tensor rank determines the complexit...")
22:25, 21 March 2023 Walle talk contribs created page Tensor Processing Unit (TPU) (Created page with "{{see also|Machine learning terms}} ==Introduction== A '''Tensor Processing Unit (TPU)''' is a specialized type of hardware accelerator designed specifically for the efficient execution of machine learning tasks, particularly deep learning algorithms. TPUs were first introduced by Google in 2016 and have since become an essential component in the field of artificial intelligence (AI) and machine learning (ML) for their ability to perform high-throughput mathematical oper...")
22:24, 21 March 2023 Walle talk contribs created page TensorFlow Serving (Created page with "{{see also|Machine learning terms}} ==Introduction== TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments. Developed by Google, it is part of the larger TensorFlow ecosystem, an open-source machine learning library used to develop, train, and deploy ML models. TensorFlow Serving provides a standardized interface for deploying and serving machine learning models, enabling easy integrati...")
22:24, 21 March 2023 Walle talk contribs created page TensorFlow Playground (Created page with "{{see also|Machine learning terms}} ==TensorFlow Playground== TensorFlow Playground is an interactive, web-based visualization tool for exploring and understanding neural networks. Developed by the TensorFlow team at Google, this tool allows users to visualize and manipulate neural networks in real-time, providing a deeper understanding of how these models work and their underlying principles. The TensorFlow Playground is an invaluable educational resource for those inte...")
22:24, 21 March 2023 Walle talk contribs created page TensorFlow (Created page with "{{see also|Machine learning terms}} ==Overview== TensorFlow is an open-source software library developed by the Google Brain team primarily for machine learning, deep learning, and numerical computation. It uses data flow graphs for computation, where each node represents a mathematical operation, and each edge represents a multi-dimensional data array (tensor) that flows between the nodes. TensorFlow provides a flexible platform for designing, training, and deployin...")
22:24, 21 March 2023 Walle talk contribs created page TensorBoard (Created page with "{{see also|Machine learning terms}} ==Introduction== TensorBoard is an open-source, interactive visualization tool designed for machine learning experiments. Developed by the Google Brain team, TensorBoard is an integral component of the TensorFlow ecosystem, which facilitates the monitoring and analysis of model training processes. It provides users with graphical representations of various metrics, including model performance, variable distributions, and comput...")
22:24, 21 March 2023 Walle talk contribs created page Tensor (Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, a '''tensor''' is a mathematical object that generalizes the concepts of scalars, vectors, and matrices. Tensors are extensively used in machine learning and deep learning algorithms, particularly in the development and implementation of neural networks. They provide a flexible and efficient way to represent and manipulate data with multiple dimensions, allowing for the efficient execution of c...")
22:24, 21 March 2023 Walle talk contribs created page TPU worker (Created page with "{{see also|Machine learning terms}} ==Overview== A '''TPU worker''' refers to a specific type of hardware device known as a Tensor Processing Unit (TPU), which is utilized in the field of machine learning to accelerate the training and inference of deep neural networks. TPUs are application-specific integrated circuits (ASICs) developed by Google and optimized for their TensorFlow machine learning framework. TPU workers are designed to perform tensor computations...")
22:24, 21 March 2023 Walle talk contribs created page TPU type (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, a ''Tensor Processing Unit'' (TPU) is a specialized type of hardware designed to accelerate various operations in neural networks. TPUs, developed by Google, have gained significant traction in the deep learning community due to their ability to provide high-performance computation with reduced energy consumption compared to traditional GPUs or Central P...")
22:23, 21 March 2023 Walle talk contribs created page TPU slice (Created page with "{{see also|Machine learning terms}} ==Introduction== A '''TPU slice''' refers to a specific portion of a Tensor Processing Unit (TPU), which is a type of specialized hardware developed by Google to accelerate machine learning tasks. TPUs are designed to handle the computationally-intensive operations commonly associated with deep learning and neural networks, such as matrix multiplications and convolutions. TPU slices are integral components of the TPU archit...")
22:23, 21 March 2023 Walle talk contribs created page TPU resource (Created page with "{{see also|Machine learning terms}} ==Introduction== The TPU, or Tensor Processing Unit, is a specialized type of hardware developed by Google for the purpose of accelerating machine learning tasks, particularly those involving deep learning and artificial intelligence. TPUs are designed to deliver high performance with low power consumption, making them an attractive option for large-scale machine learning applications. ==Architecture and Design== ===Overview=== Th...")
22:23, 21 March 2023 Walle talk contribs created page TPU node (Created page with "{{see also|Machine learning terms}} ==Introduction== A '''Tensor Processing Unit (TPU) node''' is a specialized hardware accelerator designed to significantly accelerate machine learning workloads. Developed by Google, TPUs are optimized for tensor processing, which is the foundational mathematical operation in various machine learning frameworks such as TensorFlow. By providing dedicated hardware for these calculations, TPUs enable faster training and inference of m...")
22:23, 21 March 2023 Walle talk contribs created page TPU master (Created page with "{{see also|Machine learning terms}} ==Introduction== The '''TPU master''' in machine learning refers to the primary control unit of a Tensor Processing Unit (TPU), which is a specialized hardware accelerator designed to significantly speed up the execution of machine learning tasks. TPUs were developed by Google to improve the performance of deep learning algorithms and reduce their training and inference times. The TPU master coordinates the flow of data and instruc...")
22:23, 21 March 2023 Walle talk contribs created page TPU device (Created page with "{{see also|Machine learning terms}} ==Introduction== A '''Tensor Processing Unit (TPU)''' is a type of application-specific integrated circuit (ASIC) designed and developed by Google specifically for accelerating machine learning tasks. TPUs are custom-built hardware accelerators optimized to handle the computational demands of machine learning algorithms, particularly deep learning and neural networks. They provide significant performance improvements and en...")
22:23, 21 March 2023 Walle talk contribs created page TPU chip (Created page with "{{see also|Machine learning terms}} ==Introduction== The '''Tensor Processing Unit''' ('''TPU''') is a type of application-specific integrated circuit (ASIC) designed by Google specifically for accelerating machine learning workloads. TPUs are optimized for the computational demands of neural networks and are particularly efficient at performing operations with tensors, which are multi-dimensional arrays of data commonly used in machine learning applications. TPU...")
22:22, 21 March 2023 Walle talk contribs created page TPU Pod (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, a '''TPU Pod''' is a cluster of Tensor Processing Units (TPUs) designed to accelerate high-performance computation tasks. TPUs are specialized hardware accelerators developed by Google, specifically optimized for performing tensor-based mathematical operations commonly used in machine learning and deep learning algorithms. TPU Pods allow researchers and engineers to scale up their...")
22:22, 21 March 2023 Walle talk contribs created page TPU (Created page with "{{see also|Machine learning terms}} ==Overview== A '''Tensor Processing Unit (TPU)''' is a type of application-specific integrated circuit (ASIC) developed by Google for accelerating machine learning workloads. TPUs are designed to perform tensor computations efficiently, which are the foundational operations in machine learning algorithms, particularly deep learning models. They are optimized for handling large-scale matrix operations with low precision, enabling fa...")
01:15, 21 March 2023 Walle talk contribs created page Self-supervised learning (Created page with "{{see also|Machine learning terms}} ==Introduction== Self-supervised learning (SSL) is a subfield of machine learning that focuses on learning representations of data in an unsupervised manner by exploiting the structure and inherent properties of the data itself. This approach has gained significant traction in recent years, as it enables algorithms to learn useful features from large volumes of unlabeled data, thereby reducing the reliance on labeled datasets. The lear...")
01:15, 21 March 2023 Walle talk contribs created page Selection bias (Created page with "{{see also|Machine learning terms}} ==Introduction== Selection bias in machine learning refers to the phenomenon where the sample data used to train or evaluate a machine learning model does not accurately represent the underlying population or the target domain. This issue arises when the training data is collected or selected in a way that introduces systematic errors, which can lead to biased predictions or conclusions when the model is applied to real-world scena...")
01:15, 21 March 2023 Walle talk contribs created page Scoring (Created page with "{{see also|Machine learning terms}} ==Overview== In the field of machine learning, scoring refers to the process of evaluating a trained model's performance based on its ability to make predictions on a given dataset. The scoring process typically involves comparing the model's predictions to the actual or true values, also known as ground truth or targets. A variety of evaluation metrics are used to quantify the model's performance, with the choice of metric often d...")
01:15, 21 March 2023 Walle talk contribs created page Scikit-learn (Created page with "{{see also|Machine learning terms}} ==Introduction== '''Scikit-learn''' is an open-source Python library designed for use in the field of machine learning. The library provides a wide range of machine learning algorithms, including those for classification, regression, clustering, dimensionality reduction, and model selection. Developed by a team of researchers and engineers, scikit-learn is built on top of the NumPy, SciPy, and matplotlib libraries,...")
01:14, 21 March 2023 Walle talk contribs created page Scaling (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, scaling refers to the process of adjusting the range of input features or data points to a uniform scale. This normalization of data is an essential pre-processing step that enhances the performance and efficiency of machine learning algorithms by addressing issues of heterogeneity and uneven distribution of features. ==Importance of Scaling in Machine Learning== Scaling is a crit...")
01:14, 21 March 2023 Walle talk contribs created page Scalar (Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, a ''scalar'' refers to a single numerical value that can represent a quantity or measurement. Scalars play a crucial role in many aspects of machine learning algorithms, from representing weights and biases in neural networks to serving as input features or output labels in various machine learning models. This article will cover the definition, importance, and usage of scalars in machine learn...")
01:14, 21 March 2023 Walle talk contribs created page Sampling bias (Created page with "{{see also|Machine learning terms}} ==Introduction== Sampling bias in machine learning is a type of bias that occurs when the data used for training and testing a model does not accurately represent the underlying population. This can lead to a model that performs poorly in real-world applications, as it is not able to generalize well to the broader population. In this article, we will discuss the various causes and types of sampling bias, the consequences of samplin...")
01:14, 21 March 2023 Walle talk contribs created page Root directory (Created page with "{{see also|Machine learning terms}} ==Root Directory in Machine Learning== In the context of machine learning, the term "root directory" does not directly refer to a specific concept or technique. Instead, it is related to file and folder organization in computer systems, which is crucial for managing datasets, code, and resources for machine learning projects. In this article, we will discuss the concept of a root directory in the context of computer systems and how it...")
01:14, 21 March 2023 Walle talk contribs created page Ridge regularization (Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, regularization is a technique used to prevent overfitting and improve the generalization of models by adding a penalty term to the objective function. Ridge regularization, also known as L2 regularization or Tikhonov regularization, is a specific type of regularization that adds a squared L2-norm of the model parameters to the loss function. This article discusses the underlying principles of ridge...")
01:14, 21 March 2023 Walle talk contribs created page Representation (Created page with "{{see also|Machine learning terms}} ==Introduction== Representation in machine learning refers to the method by which a model captures and encodes the underlying structure, patterns, and relationships present in the input data. A suitable representation allows the model to learn and generalize from the data effectively, enabling it to make accurate predictions or perform other tasks. Representations can be hand-crafted features, which are based on expert knowledge, o...")