Multitask learning (MTL) is a machine learning approach that involves training a model to perform multiple tasks simultaneously using the same input features. In other words, instead of introducing a separate model for each task, MTL trains a single model that can perform multiple tasks. The idea behind MTL is…

Parameters In machine learning, a model parameter refers to a configuration variable that is internal to the model and is used to make predictions on new data. Model parameters are learned from training data, which is input to a machine learning algorithm. The algorithm uses the training data to adjust the…

Natural Language Processing (NLP) is a subfield of Artificial Intelligence (AI) and computer science that focuses on enabling computers to process, understand, and generate human language. …

Self-attention is a mechanism used in machine learning, particularly in natural language processing (NLP), that allows a model to weigh the importance of different parts of an input sequence when making predictions or generating outputs. Introduction Self-attention is a mechanism that allows a neural network to selectively weigh the importance of…

BERT stands for Bidirectional Encoder Representations from Transformers. It is a state-of-the-art natural language processing (NLP) model developed by Google researchers in 2018. BERT uses a type of deep learning architecture called Transformers, which allows it to understand the context and meaning of words in a sentence or passage. …

An image dataset is a collection of digital images that are organized and labeled for use in machine learning and computer vision tasks. These datasets are used to train and test computer vision algorithms, such as object recognition, image classification, and segmentation. …

Introducing well-known Image datasets for developing machine learning algorithms Image data refers to a collection of digital images that are stored and processed by computers. MNIST is a commonly used dataset for image classification tasks in machine learning and computer vision. It consists of 70,000 grayscale images of handwritten digits (0–9) and their corresponding labels. These images are pre-processed…

TensorFlow is an open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library and is also used for machine learning applications such as neural networks. It was developed by the Google Brain team and is used in many of their projects…

Batch Normalization is a technique in deep learning used to normalize the inputs of each layer in a network, in order to reduce internal covariate shift and improve the training speed of the model. It normalizes the inputs by subtracting the batch mean and dividing by the batch standard deviation…

Schedule learning rate refers to the method of gradually decreasing the learning rate over time during the training of a deep learning model. This technique can help to improve the stability and accuracy of the model by allowing it to converge to an optimal solution more smoothly. The schedule of…

Generative Adversarial Network A Generative Adversarial Network (GAN) is a type of deep learning architecture composed of two neural networks: a generator and a discriminator. The generator is trained to generate new data samples that are similar to a given set of actual data samples. In contrast, the discriminator is trained to distinguish…

Fake data generated by GAN (Generative Adversarial Network) is synthetic data created by a machine learning model to resemble real-world data. The GAN model consists of two neural networks, a generator, and a discriminator, that work against each other to create synthetic data that is similar in characteristics to the…

Generative Adversarial Network (GAN) is a deep learning architecture for generative modeling, consisting of two neural networks: a generator and a discriminator. The generator generates new examples, while the discriminator evaluates whether they are real or fake. The two networks play a minimax game, where the generator tries to produce…

Segmentation in machine learning refers to the process of dividing an image, text, or other data into parts or segments to simplify the analysis or to isolate important features. The goal is to identify distinct and meaningful parts of the data for further processing or analysis. Introduction Segmentation in machine learning…

Semantic search is a type of search technology that aims to understand the meaning behind a user’s query in order to provide more relevant results. It involves analyzing the relationship between words, concepts, and phrases to determine the user’s intent and the context of the search. …

Hill Climb Search Hill Climbing is a heuristic optimization algorithm that iteratively moves towards a solution in a greedy manner by choosing the move that locally maximizes a given objective function. …

EfficientNet is a family of convolutional neural network (CNN) models that are designed to achieve high accuracy and efficiency in image classification tasks. These models are developed by Google Research and are based on the idea of scaling up CNN architectures in a systematic and principled way, while also making…

Introduction A convolutional neural network (CNN) is a type of deep learning neural network that is used for image and video recognition tasks. CNNs are composed of multiple layers, including convolutional layers, pooling layers, and fully connected layers. The convolutional layers are responsible for detecting patterns in the input data, while…

Introduction PyTorch has received too many interests in recent years because of its flexibility to build large-scale deep-learning frameworks. As in the computer science area, we move forward, we categorize the fields of artificial intelligence into two groups, machine learning, and deep learning. There are different libraries for building deep models…

Point Cloud Quality Assessment (PCQA) has achieved remarkable attention in recent years and applied to different point cloud media formats [1, 2]. In the case of FR or Full-Reference, we need to have a reference point cloud to achieve a metric to evaluate the quality of the point cloud. …