Broadcasting
Broadcasting is the set of rules that lets element-wise operations (addition, subtraction, multiplication, division) act on arrays or tensors of different but compatible shapes by virtually stretching the…
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Showing 1-11 of 11 articles
Convolution
Convolution is a mathematical operation that combines two functions to produce a third function, expressing how the shape of one is modified by the other.
Cross-Entropy
Cross-entropy is a measure from information theory of how many bits (or nats) are needed to encode data drawn from a true probability distribution P when using a code optimized for a different
Geometric Deep Learning
Geometric deep learning (GDL) is an area of deep learning that designs neural networks around the structure and symmetries of their data.
Inductive bias
Inductive bias (also called learning bias) is the set of assumptions that a learning algorithm uses to predict outputs for previously unseen inputs.
Manifold Hypothesis
The manifold hypothesis is the conjecture that real-world high-dimensional data, such as natural images, speech, and text representations, concentrates on or near a manifold of much lower dimension embedded…
Matrix multiplication
Matrix multiplication combines an M by K matrix A with a K by N matrix B to produce an M by N matrix C
Sigmoid Function
The sigmoid function is a mathematical function that maps any real number to a value between 0 and 1 using the formula $$\sigma(x) = \frac{1}{1 + e^{-x}}$$, producing a smooth S-shaped curve.
Tensor
In machine learning, a tensor is a multi-dimensional array of numbers that serves as the fundamental data structure for representing and manipulating data.
Tensor Shape
A tensor shape is a tuple of integers that describes the number of elements along each dimension (or axis) of a tensor.
Wasserstein GAN (WGAN)
A Wasserstein GAN (WGAN) is a generative adversarial network that trains its two networks to minimise the Wasserstein-1 distance, also called the Earth mover's distance, between the real data distribution and…