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.”
INT8
/ɪnˈteɪt/
n. “small numbers, absolute certainty.”
INT8 is an 8-bit two's complement integer ranging from -128 to +127, optimized for quantized neural network inference where model weights/activations rounded to nearest integer maintain >99% accuracy versus FP32 training. Post-training quantization or quantization-aware training converts FP32 networks to INT8, enabling 4x throughput and 4x memory reduction on edge TPUs while zero-point offsets handle asymmetric activation ranges.
Key characteristics of INT8 include:
RNN
/ɑr ɛn ˈɛn/
n. "Neural network with feedback loops maintaining hidden state across time steps for sequential data processing."
Computer-Aided Design
/kæd/
n. “The use of computers to design, model, and analyze objects before they exist.”
CAD, short for Computer-Aided Design, refers to the use of software to create precise drawings, models, and technical documentation for physical objects, structures, or systems. CAD replaces or augments manual drafting by enabling designers and engineers to work with exact measurements, constraints, and repeatable modifications.
3D
/ˌθriː-diː/
n. “The perception or representation of objects with depth, height, and width.”
3D, short for three-dimensional, refers to any object, environment, or representation that has length, width, and depth, allowing for realistic perception of volume and space. In computing and media, 3D is widely used in graphics, modeling, printing, and animation to create lifelike visuals and immersive experiences.
Key characteristics of 3D include: