One-shot learning Learning in human-like way, in one shot: learning something definite from each example. The evolving model Comparing an initial model example, a seed, with a near miss or another example, the evolving model understands an important characteristic for each…
14. Learning: Sparse Spaces, Phonology
Visual recognition to sound production Words can be classified in tables representing the 14 features of each sound, notably if they are “voiced” or not. With such a matrix of sounds, a “speaking machine” can be built to produce words…
13. Learning: Genetic Algorithms
Mimicking chromosomes Reproduction loop Strings of 0 and 1 simulate chromosomes in binary systems. An initial population of chromosomes is allowed to mutate (modification of certain bit in the strings), cross over with other strings during replication, or to remain…
12b: Deep Neural Nets
Image recognition by a deep neural net Convolution: a neuron looks for patterns in a small portion (10×10 px) of an image (256×256 px), the process is repeated by moving this small area little by litte. Pooling: The result of…
12a: Neural Nets
Modeling biological neurons Neural nets are modeled upon real biological neurons, which have the following characteristics: All or none: the input from each entry in the neural net is either O or 1, the output is also 0 or 1…