Multimodal deep learning is a subfield of artificial intelligence (AI) that focuses on developing models capable of simultaneously processing and learning from multiple types of data. This article explains its definition...
The most important development in natural language processing is Large Language Modeling (LLM). In this article, we will provide a basic introduction to LLM, discussing its definition, training methods, reasons for its popularity, common examples of LLM, and more...
Pre-training is a cornerstone of modern machine learning models. In this article, we will explore the conceptual definition of pre-training, its importance in artificial intelligence, the various techniques used to implement pre-training, and the work of researchers in this field...
A crucial aspect of machine learning is data annotation, which is the process of labeling and classifying raw data to make it usable for training ML models. This article will provide an overview of data annotation,...
Sentiment analysis is an important aspect of natural language processing that allows organizations to extract valuable insights from unstructured text data. By understanding people's opinions and sentiments, businesses, researchers, and governments can make more informed decisions...
Convolutional Neural Networks (CNNs) are a class of deep learning algorithms primarily used in computer vision. They have applications in various fields, including image and video recognition, natural language processing, etc.
Reinforcement learning from human feedback (RLHF) is an emerging research area in the field of artificial intelligence (AI) that combines reinforcement learning techniques with human feedback...
Low-rank adaptation (LoRA) technology allows for the faster and more efficient adaptation of large language models to specific tasks or domains. This article will provide an overview of what LoRA is, its main components, how it works, its advantages, and its limitations...
Apple Intelligence is a personal intelligence system developed by Apple and integrated into iPhone, iPad, and Mac platforms. It was announced at WWDC in June 2024. This AI system combines powerful generative models...
A sequence-to-sequence model, also known as an encoder-decoder model, is a deep learning model that handles variable-length input and output sequences. It consists of two parts: an encoder for processing the input...
Generative models are a class of machine learning models that learn the distribution of data and generate new samples. They create new instances that are similar to real-world data but have never appeared before by capturing patterns in the training dataset. ...
A Bayesian Network, also known as a belief network or probabilistic directed acyclic graph, is a probabilistic graphical model. It uses directed edges and nodes (representing random variables) to graphically represent the probabilistic dependencies between variables...
Domain adaptation is a method in transfer learning that aims to transfer a model from one domain (source domain) to another different domain (target domain), improving the model's performance in the target domain. Mainly...
Federated learning is a distributed machine learning technique that allows multiple participants to collaboratively train a model without sharing the original data. In this way, parties can protect data privacy and...
Embedded learning is an innovative educational model that seamlessly integrates the learning process into daily work and activities. Embedded learning posits that when learning content is closely related to work, employee learning...
AI ethics is a discipline that studies the ethical issues and risks involved in the development and application of artificial intelligence technologies. It focuses on ensuring that the design, development, and deployment of AI systems comply with ethical standards, promoting fairness, transparency, and ethical conduct.
Automated Machine Learning (AutoML) is a set of tools and techniques that automate certain tasks in a machine learning workflow, such as data preprocessing, feature engineering, model selection, hyperparameter optimization, etc.
Q-learning in reinforcement learning is a model-free reinforcement learning algorithm that finds the optimal policy by learning an action-value function Q(s, a). Given a state, Q(s, a) represents the expected total future reward for performing action a...
Neural style transfer uses deep learning to apply the visual style of one image to the content of another, creating a new image that blends the characteristics of both. It is based on pre-trained convolutional...
Semantic segmentation is a computer vision technique that uses deep learning models, especially convolutional neural networks (CNNs), to classify each pixel in an image, recognizing and distinguishing different objects...
Object detection is a key technology in computer vision, involving the identification and localization of one or more target objects from images or videos. It requires not only determining the presence of a specific object in an image, but also...
Speech recognition (ASR), also known as automatic speech recognition, is a high-tech process that converts human speech into text or commands. Through steps such as feature extraction, pattern matching, and model training, it enables machines to...
Text generation is a technique in the field of Natural Language Processing (NLP) that enables computers to automatically create readable and grammatically correct text. It involves converting non-linguistic information (such as data or images) into text, or...
Adversarial attacks are a type of security threat where attackers intentionally add subtle perturbations to the input data, causing machine learning models, especially deep learning models, to make incorrect predictions or actions...