Introduction The AIY Vision Kit from Google lets you build your own intelligent camera that can see and recognize objects using machine learning. All of this fits in a handy little cardboard cube, powered by a Raspberry Pi. Everything you need is provided in the kit, including the Raspberry Pi.  Meet your kit Welcome! Let’s get started The following instructions show you how to assemble your AIY Vision Kit, connect to it, and run the Joy Detector demo, which recognizes faces and detects if they're smiling. Then you can try running some other demos that detect other kinds of objects with the camera. You can even install your own custom-trained TensorFlow model. Time required to build: 1.5 hours Check your kit version These instructions are for Vision Kit 1.1. Check your kit version by looking on the back of the white box sleeve in the bottom-left corner. If it says version 1.1, proceed ahead! If it doesn’t have a version number, follow the assembly instructions for the earlier version. GATHER ADDITIONAL ITEMS You’ll need some additional things, not included with your kit, to build it: Micro USB power supply: The best option is to use a USB Power supply that can provide 2.1 Amps of power via micro-USB B connector. The second-best choice is to use a phone charger that also provides 2.1A of power (sometimes called a fast charger). Don't try to power your Raspberry Pi from your computer. It will not be able to provide enough power and it may corrupt the SD card, causing boot failures or other errors. Below are two different options to connect to your kit. Choose the one that works best for you, based on what you have available: OPTION 1: USE THE AIY PROJECTS APP Choose this option if you have access to an Android smartphone and a separate computer. You’ll need: Android smartphone Windows, Mac, or Linux computer Wi-Fi connection Optional: Monitor or TV (any size will work) with a HDMI input Optional: Normal-sized HDMI cable and mini HDMI adapter OPTION 2: USE A MONITOR, MOUSE, AND KEYBOARD Choose this option if you don’t have access to an Android smartphone. You’ll need: Windows, Mac, or Linux computer Mouse Keyboard Monitor or TV (any size will work) with a HDMI input Normal-sized HDMI cable and mini HDMI adapter Adapter to attach your mouse and keyboard to the kit. Below are two different options. Adapter option A: USB On-the-go (OTG) adapter cable to convert the Raspberry Pi USB micro port to a normal-sized USB port. You can then use a keyboard/mouse combo that requires only one USB port. Adapter option B: Micro USB Hub that provides multiple USB ports to connect to any traditional keyboard and mouse. Get to know the hardware Open your kit and get to know what’s inside. Take note that the Electrical Hardware bag is underneath the Mechanical Hardware bag. List of materials IN YOUR KIT 1 Vision Bonnet x1 2 Raspberry Pi Zero WH x1 3 Raspberry Pi Camera v2 x1 4 Long flex cable x1 5 Push button x1 6 Button harness x1 7 Micro USB cable x1 8 Piezo buzzer x1 9 Privacy LED x1 10  Short flex cable x1 11 Button nut x1 12 Tripod nut x1 13  LED bezel x1 14  Standoffs x2 15 Micro SD card x1 16 Camera box cardboard x1 17 Internal frame cardboard x1   Tutorial Assembly guide

Introduction The AIY Voice Kit from Google lets you build your ow natural language processor and connect it to the Google Assistant or Cloud Speech-to-Text service, allowing you to ask questions and issue voice commands to your programs. All of this fits in a handy little cardboard cube, powered by a Raspberry Pi. Everything you need is provided in the kit, including the Raspberry Pi Meet your kit Welcome! Let’s get started The following instructions show you how to assemble your AIY Voice Kit, connect to it, and run the Google Assistant demo, which turns your kit into a voice assistant that responds to your questions and commands. Then you can try some other sample code or use the Google Cloud Speech-to-Text service, which converts spoken commands into text you can use to trigger actions in your code. Time required to build: 1.5 hours Check your kit version These instructions are for Voice Kit 2.0. Check your kit version by looking on the back of the white box sleeve in the bottom-left corner. If it says version 2.0, proceed ahead! If it doesn’t have a version number, follow the assembly instructions for the earlier version. GATHER ADDITIONAL ITEMS You’ll need some additional things, not included with your kit, to build it: 2mm flat screwdriver: For tightening the screw terminals Micro USB power supply: The best option is to use a USB Power supply that can provide 2.1 Amps of power via micro-USB B connector. The second-best choice is to use a phone charger that also provides 2.1A of power (sometimes called a fast charger). Don't try to power your Raspberry Pi from your computer. It will not be able to provide enough power and it may corrupt the SD card, causing boot failures or other errors. Wi-Fi connection Below are two different options to connect to your kit to Wi-Fi, so that you can communicate with it wirelessly. OPTION 1: USE THE AIY PROJECTS APP Choose this option if you have access to an Android smartphone and a separate computer. You’ll need: Android smartphone Windows, Mac, or Linux computer OPTION 2: USE A MONITOR, MOUSE, AND KEYBOARD Choose this option if you don’t have access to an Android smartphone. You’ll need: Windows, Mac, or Linux computer Mouse Keyboard Monitor or TV (any size will work) with a HDMI input Normal-sized HDMI cable and mini HDMI adapter Adapter to attach your mouse and keyboard to the kit. Below are two different options. Adapter option A: USB On-the-go (OTG) adapter cable to convert the Raspberry Pi USB micro port to a normal-sized USB port. You can then use a keyboard/mouse combo that requires only one USB port. Adapter option B: Micro USB Hub that provides multiple USB ports to connect to any traditional keyboard and mouse. Get to know the hardware Open your kit and get familiar with what’s inside. List of materials IN YOUR KIT 1 Voice Bonnet x1 2 Raspberry Pi Zero WH x1 3 Speaker x1 4 Micro SD card x1 5 Push button x1 6 Button nut x1 7 Button harness x1 8  Standoffs x2 9 Micro USB cable x1 10 Speaker box cardboard x1 11 Internal frame cardboard x1 Tutorial   Build your kit Connect to your kit Setup the Assistant Maker's guide More Information Further products by Google  

Description A development board to quickly prototype on-device ML products. Scale from prototype to production with a removable system-on-module (SOM). Supports TFLite No need to build models from the ground up. TensorFlow Lite models can be compiled to run on the Coral Dev Board. Scale from prototype to production Considers your manufacturing needs. The SOM can be removed from the baseboard, ordered in bulk, and integrated into your hardware. Includes a full system SOC + ML + Connectivity all on the board running a derivative of Debian Linux we call Mendel, so you can run your favourite Linux tools with this board. Supports AutoML Vision Edge Easily build and deploy fast, high-accuracy custom image classification models to your device with AutoML Vision Edge. Tech specs Edge TPU Module CPU NXP i.MX 8M SOC (quad Cortex-A53, Cortex-M4F) GPU Integrated GC7000 Lite Graphics ML accelerator Google Edge TPU coprocessor RAM 1 GB LPDDR4 Flash memory 8 GB eMMC Wireless Wi-Fi 2x2 MIMO (802.11b/g/n/ac 2.4/5GHz) Bluetooth 4.1 Dimensions 48mm x 40mm x 5mm Baseboard Flash memory MicroSD slot USB Type-C OTG Type-C power Type-A 3.0 host Micro-B serial console LAN Gigabit Ethernet port Audio 3.5mm audio jack (CTIA compliant) Digital PDM microphone (x2) 2.54mm 4-pin terminal for stereo speakers Video HDMI 2.0a (full size) 39-pin FFC connector for MIPI-DSI display (4-lane) 24-pin FFC connector for MIPI-CSI2 camera (4-lane) GPIO 3.3V power rail 40 - 255 ohms programmable impedance ~82 mA max current Power 5V DC (USB Type-C) Dimensions 88 mm x 60 mm x 24mm

Description A USB accessory that brings machine learning inferencing to existing systems. Works with Raspberry Pi and other Linux systems. Local inferencing Run on-device ML inferencing on the Edge TPU designed by Google. Works with Debian Linux Connect to any Linux-based system with an included USB Type-C cable. Supports TensorFlow lite No need to build models from the ground up. Tensorflow Lite models can be compiled to run on USB Accelerator. Tech Specs ML accelerator Google Edge TPU coprocessor Connector USB Type-C* (data/power) Dimensions 65 mm x 30 mm Compatible with Raspberry Pi boards at USB 2.0 speeds only. Supported Operating Systems Debian Linux Supported Frameworks TensorFlow Lite

 NVIDIA Jetson Nano enables the development of millions of new small, low-cost, low-power AI systems. It opens new worlds of embedded IoT applications, including entry-level Network Video Recorders (NVRs), home robots, and intelligent gateways with full analytics capabilities. A NEW DIMENSION IN AI At just 70 x 45 mm, the Jetson Nano module is the smallest Jetson device. This production-ready System on Module (SOM) delivers big when it comes to deploying AI to devices at the edge across multiple industries—from smart cities to robotics. BIG COMPUTE PERFORMANCE Jetson Nano delivers 472 GFLOPs for running modern AI algorithms fast. It runs multiple neural networks in parallel and processes several high-resolution sensors simultaneously, making it ideal for applications like entry-level Network Video Recorders (NVRs), home robots, and intelligent gateways with full analytics capabilities. SMALL POWER DEMANDS Jetson Nano frees you to innovate at the edge. Experience powerful and efficient AI, computer vision, and high-performance computing at just 5 to 10 watts. TECHNICAL SPECIFICATIONS GPU NVIDIA Maxwell™ architecture with 128 NVIDIA CUDA® cores CPU Quad-core ARM® Cortex®-A57 MPCore processor Memory 4 GB 64-bit LPDDR4 Storage 16 GB eMMC 5.1 Flash Video Encode 4K @ 30 (H.264/H.265) Video Decode 4K @ 60 (H.264/H.265) Camera 12 lanes (3x4 or 4x2) MIPI CSI-2 DPHY 1.1 (1.5 Gbps) Connectivity Gigabit Ethernet Display HDMI 2.0 or DP1.2 | eDP 1.4 | DSI (1 x2) 2 simultaneous UPHY 1 x1/2/4 PCIE, 1x USB 3.0, 3x USB 2.0 I/O 1x SDIO / 2x SPI / 4x I2C / 2x I2S / GPIOs Size 69.6 mm x 45 mm Mechanical 260-pin edge connector   Developer Kit Contents NVIDIA Jetson Nano module and carrier board Small paper card with quick start and support information Folded paper stand

******** Please note this product is an official kit from Nvidia and comes assembled unlike Developer Kit for jetson Nano - 4GB which comes with Nvidia nano SOM Industrial Board with 3 years warranty ******* With Jetson Nano Developer Kit the power of modern AI is now available for makers, learners, and embedded developers everywhere. NVIDIA® Jetson Nano™ Developer Kit is a small, powerful computer that lets you run multiple neural networks in parallel for applications like image classification, object detection, segmentation, and speech processing. All in an easy-to-use platform that runs in as little as 5 watts. It’s simpler than ever to get started! Just insert a microSD card with the system image, boot the developer kit, and begin using the same NVIDIA JetPack SDK used across the entire NVIDIA Jetson™ family of products. JetPack is compatible with NVIDIA’s world-leading AI platform for training and deploying AI software, reducing complexity and effort for developers. Get started today with the Jetson Nano Developer Kit. We look forward to seeing what you create! Developer Kit Contents NVIDIA Jetson Nano module and carrier board Small paper card with quick start and support information Folded paper stand Specifications -  GPU 128-core Maxwell CPU Quad-core ARM A57 @ 1.43 GHz Memory 4 GB 64-bit LPDDR4 25.6 GB/s Storage microSD (not included) Video Encode 4K @ 30 | 4x 1080p @ 30 | 9x 720p @ 30 (H.264/H.265) Video Decode 4K @ 60 | 2x 4K @ 30 | 8x 1080p @ 30 | 18x 720p @ 30 (H.264/H.265) Camera 2x MIPI CSI-2 DPHY lanes Connectivity Gigabit Ethernet, M.2 Key E Display HDMI and display port USB 4x USB 3.0, USB 2.0 Micro-B Others GPIO, I2C, I2S, SPI, UART Mechanical 69 mm x 45 mm, 260-pin edge connector Please refer to NVIDIA documentation for what is currently supported, and the Jetson Hardware page for a comparison of all Jetson modules.Resources Developer Kit User Guide DL Inference Benchmarks Documentation FAQ Technical Blog Wiki Software     JetPack SDK NVIDIA JetPack SDK is the most comprehensive solution for building AI applications. Flash your Jetson developer kit with the latest OS image, install developer tools for both host computer and developer kit, and install the libraries and APIs, samples, and documentation needed to jumpstart your development environment. Download JetPack > DeepStream SDK NVIDIA’s DeepStream SDK delivers a complete streaming analytics toolkit for AI-based video and image understanding, as well as multi-sensor processing. DeepStream is an integral part of NVIDIA Metropolis, the platform for building end-to-end services and solutions for transforming pixels and sensor data to actionable insights. Download DeepStream > For more tutorials with Jetson Nano, visit our Tutorials page.   Jetson Nano Developer Kit: Introduction Video In this video we will walk you through the ports and other components of the Jetson Nano Developer Kit, steps to boot, and more. For step-by-step instructions, go to the Getting Started with Jetson Nano Developer Kit.     Jetson AI Courses and Certification NVIDIA’s Deep Learning Institute delivers practical hands-on training and certification in AI at the edge for developers, educators, students and lifelong learners. Get the critical AI skills you need to thrive and advance in your career. Earn certificates when you complete these free, open-source courses. Enroll Now >     Hello AI World — Meet Jetson Nano Want to take your next project to the next level with AI? If the answer is yes, this webinar is for you. We’ll introduce you to the all new Jetson Nano™ and its latest features, making it the ideal platform for creating high-performance AI projects at the edge. Watch Now >     Community Resources and Projects Find in the Jetson Community Resources page tools and tutorials the community has created to power your development experience, and check out the Community Projects page to inspire your next project!   Deploy AI with AWS ML IOT Services on Jetson Nano Learn how to use AWS ML services and AWS IoT Greengrass to develop deep learning models and deploy on the edge with NVIDIA Jetson Nano. Create a sample deep learning model, set up AWS IoT Greengrass on Jetson Nano and deploy the sample model on Jetson Nano using AWS IoT