# Difference between revisions of "Category:Machine Learning"

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[https://en.wikipedia.org/wiki/Machine_learning From Wikipedia ] ''Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task.'' | [https://en.wikipedia.org/wiki/Machine_learning From Wikipedia ] ''Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task.'' | ||

− | ''Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning | + | ''Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.'' |

− | Actually the new solutions available for the Embedded Market | + | Actually the new solutions available for the Embedded Market allow customers to start using these solutions for ML directly on edge device by: |

*processing data and algorithms by using the local power computation (CPU, GPU, dedicated HW accelerators, FPGAs) | *processing data and algorithms by using the local power computation (CPU, GPU, dedicated HW accelerators, FPGAs) | ||

− | *processing data and | + | *processing data and algorithms by using cloud services connected to the platform on the field |

'''DAVE Embedded Systems is active in this field and ML is one of the main research interest with the aim to support customers adopting these features on their products and supporting the embedded design including these capabilities.''' | '''DAVE Embedded Systems is active in this field and ML is one of the main research interest with the aim to support customers adopting these features on their products and supporting the embedded design including these capabilities.''' | ||

In the following there is a list of examples of studies DAVE Embedded Systems is currently working on: | In the following there is a list of examples of studies DAVE Embedded Systems is currently working on: |

## Latest revision as of 10:22, 14 February 2020

From Wikipedia *Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task.*

*Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.*

Actually the new solutions available for the Embedded Market allow customers to start using these solutions for ML directly on edge device by:

- processing data and algorithms by using the local power computation (CPU, GPU, dedicated HW accelerators, FPGAs)
- processing data and algorithms by using cloud services connected to the platform on the field

**DAVE Embedded Systems is active in this field and ML is one of the main research interest with the aim to support customers adopting these features on their products and supporting the embedded design including these capabilities.**

In the following there is a list of examples of studies DAVE Embedded Systems is currently working on:

## Pages in category "Machine Learning"

The following 8 pages are in this category, out of 8 total.

### M

- MISC-TN-010: Using NXP eIQ Machine Learning Development Environment with Mito8M SoM
- MISC-TN-011: Running an Azure-generated TensorFlow Lite model on Mito8M SoM using NXP eIQ
- MISC-TN-015: Proof-of-Concept of an industrial, high-frame-rate video recording/streaming system
- ML-TN-001 - AI at the edge: comparison of different embedded platforms - Part 1
- ML-TN-001 - AI at the edge: comparison of different embedded platforms - Part 2
- ML-TN-001 - AI at the edge: comparison of different embedded platforms - Part 3
- ML-TN-001 - AI at the edge: comparison of different embedded platforms - Part 4