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Added the READme file #3

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Dec 8, 2021
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Added the READme file #3

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1 change: 0 additions & 1 deletion LICENSE
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Expand Up @@ -7,4 +7,3 @@ Permission is hereby granted, free of charge, to any person obtaining a copy of
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

8 changes: 5 additions & 3 deletions README.md
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project-template
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# HTM-Applications

The Hierarchical Temporal Memory (HTM) algorithm is based on the well understood principles and core building blocks of the [Thousand Brains Theory](https://link.springer.com/content/pdf/10.1007/s42452-021-04715-0.pdf), which is the core-model based, sensory-motor framework of intelligence putting together the neuroscience research developed at [Numenta](https://numenta.com/) founded by Jeff Hawkings. It aims to reflect the functioning of the human cortex. It focuses on three main properties: _sequence learning_, _continual learning_, and _sparse distributed representations_.

This project aims to explore the applications of HTM, specifically its [Anomaly Detection](https://numenta.com/assets/pdf/whitepapers/Numenta%20White%20Paper%20-%20Science%20of%20Anomaly%20Detection.pdf) technique, in the AIOps domain.

template for the team to use

Project Organization
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