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Merge pull request #2034 from jasonrandrews/review2
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.wordlist.txt

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normals
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svcntb
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svmatch
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tc
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tc
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Alexa
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BLERP
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Cepstral
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Datagram
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Edudzi
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GDPR
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Gbit
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Gershon
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HIPAA
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HVAC
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Kordorwu
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MCUs
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MFCC
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Mbit
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PDM
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Situnayake
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accelerometers
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iPerf
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libcrypto
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libray
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libssl
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misclassification
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retransmission
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subquery
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uninstrumented

content/learning-paths/embedded-and-microcontrollers/Egde/Software_Edge_Impulse.md

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With your device configured, the next step is to **add your dataset** to the project. Click on the **"Add existing data"** button and follow the configuration settings shown in the attached snapshot. This allows you to upload pre-recorded data instead of collecting it live, which can save time during the development phase.
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The dataset for this project can be downloaded from the following link: [Download Dataset](https://github.com/e-dudzi/Learning-Path.git). The Dataset has already been splitted into **trainig** and **testing**.
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The dataset for this project can be downloaded from the following link: [Download Dataset](https://github.com/e-dudzi/Learning-Path.git). The Dataset has already been split into **training** and **testing**.
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![example image alt-text#center](Images/6.png "Figure 4. Add Existing Data")
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content/learning-paths/embedded-and-microcontrollers/Egde/_index.md

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- Interface with LEDs to switch them on and off .
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prerequisites:
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- Explore this [learning path](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/arduino-pico/) if you are an absolute beginer.
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- Explore this [learning path](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/arduino-pico/) if you are an absolute beginner.
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- An [Edge Impulse](https://edgeimpulse.com/) Studio account.
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- The [Arduino IDE with the RP2040 board support package](https://learn.arm.com/install-guides/arduino-pico/) installed on your computer
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- An Arduino Nano RP2040 Connect [board](https://store.arduino.cc/products/arduino-nano-rp2040-connect-with-headers?_gl=1*9t4cti*_up*MQ..*_ga*NTA1NTQwNzgxLjE3NDYwMjIyODk.*_ga_NEXN8H46L5*MTc0NjAyMjI4Ny4xLjEuMTc0NjAyMjMxOC4wLjAuMjA3MjA2NTUzMA..).

content/learning-paths/embedded-and-microcontrollers/_index.md

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maintopic: true
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operatingsystems_filter:
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- Android: 1
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- Baremetal: 29
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- Baremetal: 30
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- Linux: 28
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- macOS: 6
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- RTOS: 9
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title: Embedded and Microcontrollers
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tools_software_languages_filter:
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- AI: 1
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- Arduino: 1
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- Arduino: 2
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- Arm Compiler for Embedded: 7
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- Arm Compiler for Linux: 1
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- Arm Compute Library: 1
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- DetectNet: 1
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- Docker: 9
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- DSTREAM: 2
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- Edge AI: 1
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- Edge Impulse: 1
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- ExecuTorch: 2
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- Fixed Virtual Platform: 9
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- FPGA: 1
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- STM32: 2
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- TensorFlow: 3
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- TensorRT: 1
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- tinyML: 1
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- tinyML: 2
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- Trusted Firmware: 3
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- TrustZone: 2
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- TVMC: 1

content/learning-paths/servers-and-cloud-computing/_index.md

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maintopic: true
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operatingsystems_filter:
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- Android: 2
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- Linux: 146
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- Linux: 147
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- macOS: 10
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- Windows: 14
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pinned_modules:
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- Databases: 15
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- Libraries: 9
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- ML: 27
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- Performance and Architecture: 56
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- Performance and Architecture: 57
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- Storage: 1
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- Web: 10
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subtitle: Optimize cloud native apps on Arm for performance and cost
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- Bash: 1
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- bash: 2
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- Bastion: 3
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- BOLT: 1
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- BOLT: 2
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- bpftool: 1
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- C: 4
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- C#: 2
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- Hugging Face: 9
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- InnoDB: 1
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- Intrinsics: 1
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- iperf3: 1
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- iPerf3: 1
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- Java: 3
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- JAX: 1
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- Kafka: 1
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- ONNX Runtime: 1
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- OpenBLAS: 1
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- PAPI: 1
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- perf: 4
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- perf: 5
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- Perf: 1
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- PostgreSQL: 4
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- Python: 27
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- Redis: 3
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- Remote.It: 2
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- RME: 6
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- Runbook: 70
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- Runbook: 71
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- Rust: 2
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- snappy: 1
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- Snort3: 1

content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md

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learning_objectives:
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- Understand the purpose and function of SVE2 MATCH instructions
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- Implement a search algorithm using both scalar and SVE2-based MATCH approaches
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- Benchmark and compare performance between scalar and vectorized implementations
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- Analyze speedups and efficiency gains on Arm Neoverse-based Graviton4 instances
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- Understand the purpose and function of SVE2 MATCH instructions.
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- Implement a search algorithm using both scalar and SVE2-based MATCH approaches.
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- Benchmark and compare performance between scalar and vectorized implementations.
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- Analyze speedups and efficiency gains on Arm Neoverse-based Graviton4 instances.
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prerequisites:
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- Access to an [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a supported cloud service provider
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