AI accelerator An AI accelerator is a specialized hardware component which is mainly utilized for artificial intelligence tasks. It can perform AI computations in a more efficient manner than conventional computer processors (CPU), hence AI accelerators are also known as Neural Processing Units (NPU). By using algorithms for parallelism, AI accelerators can enchance performance of ... Read more
Artificial Intelligence (AI) relates to computer hardware in multiple ways, as follows: AI platforms are being used to optimize the design and manufacturing of new compute hardware systems. Special computer hardware processor units, referred to as AI accelerators or Neural Processing Units (NPU) are being developed to enhance the performance of AI applications and machine ... Read more
An algorithm is a series of instructions, either in the form of pseudo-code or in a computer programming language, which aim at solving a problem or at performing some sort of computation. Algorithms are therefore strongly connected to various fields of mathematics and physics, such as probability and statics, calculus and linear algebra. Algorithms apply ... Read more
Artificial intelligence (AI) means developing intelligence in computing systems and robots. Artificial intelligence emulates human brain intelligence and is capable of executing tasks which require cognitive ability. The most common cognitive services which can be implemented in AI are the following: Language, including Natural Language Processing (NLP) and Natural Language Understanding (NLU) Computer vision, including ... Read more
Application-Specific Integrated Circuits (ASICs) are custom-built integrated circuit (IC) electronic systems, which are designed to optimize AI and ML workloads. ASICs have a large application spectrum, including for example video and voice codecs. In contrast to ASICs which are application specific, there are various general purpose Integrated Circuit systems, such as FPGAs.
The coefficient of determination (also depicted as R^2) is a calculation of the proportion of the variance in the dependent variable (noted as y) which is explained by a linear regression model. The value of the coefficient of determination can be in the 0..1 range. The coefficient of determination can be calculated by using the ... Read more
In machine learning (ML), cross validation is a method in which the data scientists perform an evaluation of an ML model's performance on unlabelled data, i.e. data which the ML model has not seen before. In the method of cross validation, the data which is available in the dataset is split into multiple subsets. One ... Read more
ETL stands for extract, transform, and load. It refers to a data science and machine learning procedure, in which ML model data is being collected (extracted) from data sources, then data is transformed and finally loaded into an ML model.
Graphics Processing Units (GPUs) have been traditionally used in computing systems for image processing (graphics cards). Their usage has largely been expanded to AI and ML tasks and are largely used for optimizing performance of neural networks.
In machine learning, holdout validation is a data sampling method in which the dataset is split into two: the training dataset and the test. The split is equal, i.e. training is performed on the 50% of the dataset and testing is performed on the remaining 50% of the dataset. Holdout validation is not recommended in ... Read more
In machine learning, k-fold cross validation is a type of cross validation in which the dataset is split into k subsets and the validation process is repeated k times. Each time k-1 subsets are used for training the ML model, while one (1) subset is used for testing/validation. The final validation generalization capability/performance of the ... Read more
Keras is one of the most recognized deep learning APIs. Keras has been developed in the Python programming language and can be executed on top of JAX, TensorFlow, or PyTorch. More details about Keras can be found in its official website at: https://keras.io/about/.
In machine learning, LOOCV stands for leave-one-out cross validation. Assuming that the dataset of an ML project comprises n examples (rows), we split the dataset into n subsets and perform n iterations of training/testing. In each iteration, n-1 examples are used as training data and only 1 example is used the testing subset. This process ... Read more
Machine learning Machine learning (ML) is a field of study that involves the development of algorithms and statistical models which enable computing systems to develop skills in specific areas, by using data-based training and evaluation. Commonly this leads to Artificial Intelligence (AI) cognitive services, such as computer vision and image recognition.
MAE in statistics and Machine Learning (ML) stands for the Mean Absolute Error. MAE is the average of the sum of the differences between the actual and predicted values in a dataset. In other words the MAE is the calculation of the the average of the residuals. MAE is expressed by the following mathematical formula. ... Read more
MSE in Machine Learning (ML) stands for Mean Squared Error and is an error calculation formula. MSE calculates the average value of the square power of the sum of differences between the original and predicted values in a dataset. It is similar to MAE, in that MSE is a calculation for the variance of residuals, ... Read more
Neural network An artificial neural network (aka neural network) is a computational model which is employed by machine learning algorithms and is based on connected hierarchical functions. It simulates the neurons of the human brain.
In machine learning, normalization is a statistical technique by which the data in a dataset are transformed to have values in a normal (Gaussian) distribution. For each value x in the dataset, its corresponding normalized value x' is calculated in the value range [0,1] as follows. Alternatively, there can be a mean normalization, with normalized ... Read more
NPU stands for Neural Processing Unit. It is another name for an AI accelerator.
In machine learning, PCA stands for Principal Component Analysis. PCA is used to tackle a known ML problem when a dataset has a large number of features, i.e. high dimensions, also known as the curse of dimensionality. The ML feature engineering engineering techniques available are classified into feature selection and feature extraction techniques. Dimensionality reduction ... Read more
