Few-shot learning refers to a variety of algorithms and techniques used to develop an AI model using a very small amount of training data. Few-shot learning...
Transformer Neural Networks Described Transformers are a type of machine learning model that specializes in processing and interpreting sequential data, making them optimal for natural language processing...
A group of international AI researchers and data scientists have collaborated to design software capable of estimating the carbon footprint of computing operations. The open-source software...
K-means clustering is an unsupervised learning algorithm, and out of all the unsupervised learning algorithms, K-means clustering might be the most widely used, thanks to its...
A common type of machine learning model that has managed to be extremely useful in data science competitions is a gradient boosting model. Gradient boosting is...
Some machine learning models belong to either the “generative” or “discriminative” model categories. Yet what is the difference between these two categories of models? What does...
Cybersecurity is the way in which systems, networks, and programs utilize technologies, processes, and practices to protect against digital attacks. Cyberattacks often target sensitive information and...
Natural language understanding (NLU) is a technical concept within the larger topic of natural language processing. NLU is the process responsible for translating natural, human words...
Advancements in artificial or electronic skin are fundamental to the creation of humanoid robots, as skin provides us humans with the sense of touch, the ability...
Edge AI is one of the most notable new sectors of artificial intelligence, and it aims to let people run AI processes without having to be...
One of the most powerful machine learning techniques is ensemble learning. Ensemble learning is the use of multiple machine learning models to improve the reliability and...
What is Dimensionality Reduction? Dimensionality reduction is a process used to reduce the dimensionality of a dataset, taking many features and representing them as fewer features....
Generative Adversarial Networks (GANs) are types of neural network architectures capable of generating new data that conforms to learned patterns. GANs can be used to generate...
If you’ve read about unsupervised learning techniques before, you may have come across the term “autoencoder”. Autoencoders are one of the primary ways that unsupervised learning...
What is Synthetic Data? Synthetic data is a quickly expanding trend and emerging tool in the field of data science. What is synthetic data exactly? The...
