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A RANGE OF MACHINE LEARNING TECHNIQUES THAT ADVANCE TECHNOLOGY HUMANKIND

Hypergiant collaborates with Fortune 500s to invent Machine Intelligence that assimilates the latest Machine Learning techniques, enabling businesses to respond to emerging markets and humans to reach their fullest potential.

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Modern Machine Learning is not about teaching Artificial Intelligence how to behave how a human would; it is about endowing technology with tools that enable it to learn, for itself, how to understand unfamiliar concepts and solve problems—like the toddler who intuits gravity when she drops a block.

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Machine Learning is the result of combining big data with algorithms to produce a function that learns a pattern.


MACHINE LEARNING TYPES

  • SUPERVISED LEARNING

    The machine has a labeled, annotated, classified, or solved data set from which it answers directly and/or deduces the most likely answer based on existing variables.

  • UNSUPERVISED LEARNING

    The machine has data that is not labeled, annotated, classified, or solved. It analyzes the data set to determine structure, patterns, connections, and categorizations.

  • REINFORCEMENT LEARNING

    The machine learns its environment, resources, objectives, and success or failure through trial and error then makes future decisions based on its findings.

MACHINE LEARNING TECHNIQUES

UNSUPERVISED LEARNING TECHNIQUES

Adaptive Resonance Theory (ART)

  • learning from new inputs (adaptive) without forgetting previous information (resonance)—e.g. recognizing an old friend after meeting new people

Clustering

  • grouping data points that have similar features or statistical observations—e.g. grouping pharmaceuticals by medicinal properties

Dimensionality Reduction

  • decreasing variables in a dataset by identifying essential variables and excluding all others—e.g. identifying a specific object amidst a busy backdrop

Self-Organizing Maps (SOM)

  • a process of dimensionality reduction that creates low-dimensional graphics of data—e.g. a topographical map of ground composition and depth for oil extraction

SUPERVISED LEARNING TECHNIQUES

Convolutional Neural Network (CNN)

  • neural network that identifies and classifies data by frames of increasing complexity—e.g. identifying letters in a handwritten note one pen stroke at a time

Decision Trees (DT)

  • a series of if this, then that paths that guide data processing based on the achieved output—e.g. automated, multi-factor authentication for an online app

k-Nearest Neighbor (k-NN)

  • making predictions about unknown data by classifying it according to the nearest data with known traits—e.g. determining if a new snake species is poisonous

Naïve Bayesian

  • calculating probability by multiplying the sub-probabilities (inputs) of a greater outcome—e.g. deducing whether a candidate is Democrat or Republican

Neural Networks (NN)

  • modeled after the human brain and nervous system; a group of neurons makes up a layer, and calculations traverse layers as neurological signals across synapses

Recurrent Neural Network (RNN)

  • neural network that recycles data through its hidden layers, adding memory-type context to its learning—e.g. learning to translate a foreign language

Statistical Regression

  • a data analytics model that makes predictions based on relationships it identifies between two or more variables—i.e. Correlative Analytics

Support Vector Machines (SVM)

  • making predictions about unknown data that has near-identical features to two or more known datasets—e.g. classifying new food as a fruit or vegetable

REINFORCEMENT LEARNING TECHNIQUES

Genetic Algorithms

  • algorithms based on biological or natural processes such as natural selection or gene mutation—e.g. simulating the metastasis of cancer cells

Particle Swarm Optimization (PSO)

  • algorithms that mimic the natural patterns of animals and insects that flock or swarm—e.g. a group of robots that searches rubble for victims

Q-Learning

  • devising a function that identifies the optimal path to a reward, minimizing failures, without a model of the environment—e.g. a robot learning to walk

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DATA ANALYTICS

In this technological era of accelerated, exponential growth, we amass incredible amounts of data—which makes processing by anyone who is not a superhuman impossible.

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Hypergiant applies two major data analytics models to its Machine Learning techniques with the goals of honing analysis, increasing processing speed, and making more precise predictions.

PREDICTIVE ANALYTICS......

  • A method in data analysis that predicts future probabilities based on patterns and trends in historical data |

CORRELATIVE ANALYTICS……

  • A method in data analysis that discovers correlations in data to make predictions without knowing exact rules or theories of the solution |
Machine Intelligence Techniques