What is skip-gram? Skip-gram is a popular algorithm used in natural language processing (NLP), specifically in word embedding techniques. It is a method for learning word representations in a vector...
What is skip-gram? Skip-gram is a popular algorithm used in natural language processing (NLP), specifically in word embedding techniques. It is a method for learning word representations in a vector...
What is fuzzy name matching? A fuzzy name matching algorithm, or approximate name matching, is a technique used to compare and match names with slight differences, variations, or errors. It is...
Graph Neural Network (GNN) is revolutionizing the field of machine learning by enabling effective modelling and analysis of structured data. Originally designed for graph-based data, GNNs have found...
What is few-shot learning? Few-shot learning is a machine learning technique that aims to train models to learn new tasks or recognise new classes of objects using only a small amount of labelled...
History of natural language processing Natural Language Processing (NLP) has a fascinating history that spans several decades. Let's journey through time to explore the key milestones and...
What is an activation function? In artificial neural networks, an activation function is a mathematical function that introduces non-linearity to the output of a neuron or a neural network layer. It...
Why Combine Numerical Features And Text Features? Combining numerical and text features in machine learning models has become increasingly important in various applications, particularly natural...
What are open-source large language models? Open-source large language models, such as GPT-3.5, are advanced AI systems designed to understand and generate human-like text based on the patterns and...
L1 and L2 regularization are techniques commonly used in machine learning and statistical modelling to prevent overfitting and improve the generalization ability of a model. They are regularization...
What is hyperparameter tuning in machine learning? Hyperparameter tuning is critical to machine learning and deep learning model development. Machine learning algorithms typically have specific...
What is CountVectorizer in NLP? CountVectorizer is a text preprocessing technique commonly used in natural language processing (NLP) tasks for converting a collection of text documents into a...
Unstructured data has become increasingly prevalent in today's digital age and differs from the more traditional structured data. With the exponential growth of information on the internet, the vast...
The F1 score formula The F1 score is a metric commonly used to evaluate the performance of binary classification models. It is a measure of a model's accuracy, and it takes into account both...
Classification vs regression are two of the most common types of machine learning problems. Classification involves predicting a categorical outcome, such as whether an email is spam or not, while...
Latent Dirichlet Allocation explained Latent Dirichlet Allocation (LDA) is a statistical model used for topic modelling in natural language processing. It is a generative probabilistic model that...
What is GPT-3? GPT-3 (Generative Pre-trained Transformer 3) is a state-of-the-art language model developed by OpenAI, a leading artificial intelligence research organization. GPT-3 is a deep neural...
Endogenous and exogenous variables are two important concepts. In machine learning, endogenous variables are the variables that are directly influenced by other variables within the system being...
Natural Language Processing (NLP) has become an essential area of research and development in Artificial Intelligence (AI) in recent years. NLP models have been designed to help computers...
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