Arm NN

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Run and profile Arm NN on the HiKey 960

Use Streamline to profile the performance of Linux applications that run TensorFlow models on Arm devices

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Configure the Arm NN SDK build environment for Caffe

Download, set up, build, and test Arm NN and its dependencies for Caffe


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Configure the Arm NN SDK build environment for Onnx

Download, set up, build, and test Arm NN and its dependencies for ONNX



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Configure the Arm NN SDK build environment for TensorFlow

Download, set up, build, and test Arm NN and its dependencies for TensorFlow

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Configure the Arm NN SDK build environment for TensorFlow Lite

Download, set up, build, and test Arm NN and its dependencies for TensorFlow Lite

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Deploy a Caffe MNIST model using the Arm NN SDK

Import a Caffe model into Arm NN, optimize and load it onto a compute device

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Improve your ML workflow using the Arm NN SDK

Walk through a typical ML workflow with a 32-bit floating-point Convolutional Neural Network

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Quantize neural networks to 8-bit using TensorFlow

Improve the performance of neural network models on Arm compute devices using TensorFlow





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Deploy a quantized TensorFlow Lite MobileNet V1 model

Develop a lightweight image classification mobile application using the Arm NN SDK

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Deploy a TensorFlow MNIST model using the Arm NN SDK

                                       Import a TensorFlow model into Arm NN, optimize and load it onto a compute device                                                

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Machine learning on Cortex-M

Build an Arm Cortex-M voice assistant with Google TensorFlow Lite

Perform machine learning inference on an Arm Cortex-M microcontroller with TensorFlow Lite for Microcontrollers

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Convert a neural network for Arm Cortex-M with CMSIS-NN

Convert a neural network from any framework into an implementation on an Arm Cortex-M device

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Perform image recognition on Arm Cortex-M with CMSIS-NN

Improve the performance and energy efficiency of real-time image recognition on an Arm Cortex-M7 processor

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Deploy a Caffe model on OpenMV using CMSIS-NN

Run a smile detection program on an Arm Cortex-M7 processor using a Caffe model

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Machine learning on Raspberry Pi

Cross-compile Arm NN and TensorFlow for the Raspberry Pi 

Cross-compile Arm NN on an x86_64 system to work around the limited memory of the Raspberry Pi





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Run AlexNet on Raspberry Pi with Arm Compute Library

Develop Convolutional Neural Networks using just the Compute Library and a Raspberry Pi






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Profile AlexNet on Raspberry Pi and HiKey 960

Use Streamline to profile the performance of an AlexNet sample application on two hardware platforms

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Teach your Raspberry Pi

This series of beginner level guides teaches you how to implement simple machine learning on your Raspberry Pi for gesture recognition that you can use to control the Pi.

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Teach your Raspberry Pi - Episode 1 Yeah, World

Train your Raspberry Pi to burst into applause when you raise your hands in celebration

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Teach your Raspberry Pi - Episode 2 Multi-gesture Recognition

Train your network to recognize gestures in many situations and learn how to use larger datasets

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Other topics

Optimizing neural networks for mobile and embedded devices with TensorFlow

Prepare TensorFlow models for deployment on Android, Linux, and iOS

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Deploying cloud-based ML for speech transcription

Set up client-server speech transcription deployed as a service running on cloud-hosted Arm servers

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