Machine Learning
/məˈʃiːn ˌlɜːrnɪŋ/
noun … “teaching machines to improve by experience instead of explicit instruction.”
Machine Learning is a branch of computer science focused on building systems that can learn patterns from data and improve their performance over time without being explicitly programmed for every rule or scenario. Rather than encoding fixed logic, a machine learning system adjusts internal parameters based on observed examples, feedback, or outcomes, allowing it to generalize beyond the data it has already seen.
R
/ɑːr/
noun … “a language that turns raw data into statistically grounded insight with ruthless efficiency.”
Encryption
/ɪnˈkrɪpʃən/
noun … “the process of transforming data into a form that is unreadable without authorization.”
IPC
/ˌaɪ piː ˈsiː/
noun … “a set of methods enabling processes to communicate and coordinate with each other.”
acknowledgment
/əkˌnɒlɪdʒˈmɛnt/
noun … “a signal or message confirming that data has been successfully received.”
acknowledgment is a critical concept in computing and networking that ensures reliable communication between systems or processes. When one system sends data, the recipient responds with an acknowledgment (often abbreviated as ACK) to confirm that the information has been successfully received, processed, or queued. This mechanism prevents data loss, supports error detection, and enables retransmission in case of failures.
VAE
/ˌviː.eɪˈiː/
noun … “a probabilistic neural network that learns latent representations for generative modeling.”
Generative Pre-trained Transformer
/ˌdʒiːˌpiːˈtiː/
noun … “a generative language model that predicts and produces coherent text.”
Autoencoder
/ˈɔːtoʊˌɛnˌkoʊdər/
noun … “a neural network that learns efficient data representations by reconstruction.”
Transformer
/trænsˈfɔːrmər/
noun … “a neural network architecture that models relationships using attention mechanisms.”
Convolutional Neural Network
/ˌsiːˌɛnˈɛn/
noun … “a deep learning model for processing grid-like data such as images.”