Artificial intelligence – about artificial intelligence – part 001.

The purpose of this tutorial series is to give you an introduction to AI, presenting only the essentials for any user.
A fairly correct definition from my point of view is the one presented on Wikipedia:
Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by non-human animals and humans…
Common data structures used in artificial intelligence are:
Stack: a one-dimensional data structure with a defined size. This is the common stack.
Queue: a one-dimensional data structure with a defined size.
Linked List: a one-dimensional data structure with a variable size. Each element, or node, in the linked list, contains a reference to the next element
Tree: a multi-dimensional data structure with a variable size where each node in the tree can have one or more child nodes, forming branches.
Graph: a multi-dimensional data structure with a variable size consists of a set of vertices (or nodes) and edges connecting them.
Tensor: multi-dimensional data structure with a variable size, mostly used in deep learning and numerical computation.
These can be:
– dense tensors are the most common type of tensors and are represented by a multi-dimensional array of numbers, where each element in the array has a corresponding value.
– sparse tensors are tensors that contain mostly zeros, with only a small number of non-zero values.
– complex tensor which is the generalization of tensors that can have complex numbers as entries.
– hypercomplex tensor which is a tensor whose entries are hypercomplex numbers.
– hypercomplex numbers are not as widely used as complex numbers in AI, but they are used in certain specific applications such as computer vision, robotics, and physics.
Set: an unordered collection with a variable size.
Hash Table: a data structure that uses a hash function to map keys to values, which can have a variable size depending on the number of elements stored in it.
Heap: a multi-dimensional data structure with variable size and typically implemented as binary trees.
Bloom Filter: a fixed-size data structure, which is a probabilistic data structure for testing whether an element is a member of a set or not.

tensor with video data can have a 3-dimensional tensor used to represent a video, where the first dimension represents the time axis, the second dimension represents the height of each frame, and the third dimension represents the width of each frame.

hypercomplex numbers, which are defined as a + bi + cj + dk, where a, b, c, and d are real numbers and i, j, and k are the three imaginary units can be quaternions to represent rotations in 3D space.

The result of this source code is this: quaternion(0, 0.103846565151668, 0.422918571742548, 0.900197629735517).

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