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Tensorflow Android Example

tensorflow 소스코드에는 Android 예제가 있다. tensorflow) submitted 11 months ago by 1cmanny1 I have a model that looks at images and draws boxes around the detected object. (libtensorflow. The demo app supports both the quantized model and the float model. Hello World! on Android¶ Hello World! on Android should be the first mobile example users go through in detail. We are excited to release a TensorFlow Lite sample application for human pose estimation on Android using the PoseNet model. model() APIs of TensorFlow. The era of Deep Learning and Machine Learning is at its peak. Then TensorFlow can delegate separate parts of the graph to separate GPUs to perform the calculation and then combine the results. For example, in this. Run a local training job A local training job loads your Python training program and starts a training process in an environment that's similar to that of a live AI Platform cloud training job. The app can run on either a real Android device or in the Android Studio Emulator. If you examine the tensorflow repo on GitHub, you’ll find a little tensorflow/examples/android directory. Command line. Consider a single element of the bias vector being added to a single element from the matmul result, for example. The environment If your primary area of focus is mobile engineering, it’s pretty likely you don’t have python environment with all required libraries to start working with TensorFlow. This library also provides us a great API on the Android, Image Classification API. iPhone 8, Pixel 2, Samsung G. PoseNet is a vision model that estimates the pose of a person in an. Android Demo: An Android app using a TensorFlow Lite version of mobile net. Can you please guide me on that? I want to use C++ apis on Android in the similar way as done in iOS examples. The recommendation system in the tutorial uses the weighted alternating least squares (WALS) algorithm. Images gathered from the internet will be of different sizes. Take a tour through the AIY Vision Kit with James, AIY Projects engineer, as he shows off some cool applications of the kit like the Joy Detector and object classifier. The examples assume you have imported the TensorFlow model as in the example above. Creating an Object Detection Application Using TensorFlow This tutorial describes how to install and run an object detection application. The new library will allow. Google Developers Codelabs provide a guided, tutorial, hands-on coding experience. Example using TensorFlow Estimator, Experiment & Dataset on MNIST data. But to classify the data, we must have a set of probabilities—we make a final decision. For example: Many of the operations in this graph can be fused into a single element-wise loop. I'm aware of the other questions similar to this one on StackOverflow, but none of them seem to address the problems I've run into. Describes the sample applications made for AI Platform. And according to payscale. tensorflow/models Models and examples built with TensorFlow. Is there any tutorial / article / anything else in order to help me implement TensorFlow?. 0 or higher; TensorFlow 1. TensorFlow is an open-source machine learning library for research and production. It teaches the following: Introduction of a simple MediaPipe graph running on mobile GPUs for Sobel edge detection. 1 by releasing updates for existing devices on March 9, 2015. The environment If your primary area of focus is mobile engineering, it’s pretty likely you don’t have python environment with all required libraries to start working with TensorFlow. Tells the story of how we managed to improve the pipeline for on-device Machine Learning in our successful Android app "Simply Piano" by using TensorFlow, and describes some of the pitfalls in doing so. Mobile is available as a NuGet package that wraps the native Android libraries. Introduction. Google brought Chrome for Android in line with the desktop version with Chrome 25. In this article I'll focus on how you use it in an Android app. transparent use of a GPU – Perform data-intensive computations much faster than on a CPU. 1, an updated version of Lollipop, was unveiled in February 2015 as part of the Indonesian launch of Android One, and is preloaded on Android One devices sold in Indonesia and the Philippines. This is an example application for TensorFlow Lite on Android. Althrough Facebook’s Torch7 has already had some support on Android, we still believe that it’s necessary to keep an eye on Google. Google I/O 2019 returns to the Shoreline Amphitheatre May 7-9. This last reason is the operating reason for this post since we’ll be focusing on Android. Once you have it, clone the TensorFlow repo (which you might already have), and in Android Studio import a new project using the directory from the TensorFlow repo you just cloned, called “Android”. In this tutorial you'll know how to run deep learning networks on Android device using OpenCV deep learning module. TensorFlow Lite enables on-device machine learning inference with low latency. Justin Francis is currently an undergraduate student at the University of Alberta in Canada. The application code is located in the Tensorflow examples repository, along with instructions for building and deploying the app. org grant to Hispanic Federation and partnership with LULAC. Select the tensorflow/examples/android directory from wherever you cloned the TensorFlow Github repo. The on-device model comes with several benefits. With the TensorFlow Lite inference library for Android, developers can easily integrate TensorFlow and machine learning into their apps on Android Things. The main reason is that, at the time of writing (July 2016), CUDA has not yet been built for the most recent Ubuntu version, which means the process is a lot more manual. TensorFlow is an end-to-end open source platform for machine learning. That is why we will build the app on top of this example. This last reason is the operating reason for this post since we’ll be focusing on Android. 9 of the open source library includes backing for iOS and Raspberry Pi and Python 3. Custom TensorFlow Lite models can also be uploaded through the Firebase console. Grow with Google New tools to help Spanish speakers build their careers. tensorflow/models Models and examples built with TensorFlow. tensorflow) submitted 1 year ago by Xe0n360 Check out our deep-learning based android app which captions live camera frames in real-time. iPhone 8, Pixel 2, Samsung G. Committed to these sources, these arrange may be a fantastic choice. Grow with Google New tools to help Spanish speakers build their careers. These examples are good starting points, so we don't have to start from scratch. The Developer preview of TensorFlow Lite is built into version 1. See the sample for Tensorflow model in an Android application for real-time image classification on Android. You can use ML Kit to perform on-device inference with a TensorFlow Lite model. This tutorial is about android sqlite database with example. The articles explain more, but I created this Docker image which contains all the dependencies you need so that you can just pull the Docker image and build the Android demo app. TensorBoard for visualization 3. model() APIs of TensorFlow. There is a TensorFlow Lite sample application that demonstrates the smart reply model on Android. TensorFlow has a GPU backend built on CUDA, so I wanted to install it on a Jetson TK1. TensorFlow is an end-to-end open source platform for machine learning. System information Have I written custom code (as opposed to using a stock example script provided in TensorFlow):No OS Platform and Distribution (e. The core of the TensorFlow library is built in C++, but the programmers can write the. As a quick overview, it supports most of the basic operators; in simple terms, you can use it to do classification, object detection, semantic segmentation, and most of. If you check nuget. Grow with Google resources are available in Spanish, along with a Google. Learn how to build deep learning applications with TensorFlow. How to train your own custom model. The example is configured to be built with Google's Bazel build system running from the TensorFlow root directory. 4 Beta java版本:open jdk 1. See the guide Guides explain the concepts and components of TensorFlow Lite. TensorFlow tutorials and examples. In this tutorial you got a brief introduction to using TensorFlow for machine learning and integrating it into an Android Things app. For example, a tensor with dimension (or rank in TensorFlow speak) 0 is a scalar, rank 1 a vector, rank 2 a matrix and so on. Mobile is available as a NuGet package that wraps the native Android libraries. In this article we use an example app called 'Hot or Not' and we'll go through each aspect of the code and explain everything step by step. Firstly, you need to download Android Studio. pb and labels. These widgets will have their width set to fill_parent. TensorFlow is an open-source software library for Machine Intelligence provided by Google. With the skills you learned today, you should have no problems using larger models, such as MobileNet and Inception, available in the TensorFlow model zoo. TensorFlow based android app which does image-captioning in real-time (self. For example: Many of the operations in this graph can be fused into a single element-wise loop. Now we have a new raspberry pi 4 model B 1GB So try to run TensorFlow object detection and then compare with Raspberry pi3B+ also. The TF Classify example, which uses the Android camera to classify images in real time, roughly corresponds to the requirements of our app. You can find tutorials on Internet. For starters, it will take an image of the fruit as input and predict whether it’s an apple or oranges as output. These can be: Voice recognition – mostly used in IoT, Automotive, Security and UX/UI. This is an app that continuously detects the body parts in the frames seen by your device's camera. TensorFlow Lite Object Detection Android Demo Overview This is a camera app that continuously detects the objects (bounding boxes and classes) in the frames seen by your device's back camera, using a quantized MobileNet SSD model trained on the COCO dataset. 1 by releasing updates for existing devices on March 9, 2015. js and later saved with the tf. Machine Learning Crash Course does not presume or require any prior knowledge in machine learning. To use the model, we need to create an instance of the TensorFlow interface using our model file. Although it doesn't get deep into any machine learning or Android concepts, you need to have a basic knowledge of Python, Java, Tensorflow, and Android development to go follow this tutorial. Weka, Solidity, Org. These steps can be applied to any model that you wish to add to your app: Include the TensorFlow Lite dependency; Create a separate class that wraps the model and it’s pre- and post- processing steps. But since Android's 5. You can edit this line in _config. An Open Source Machine Learning Framework for Everyone - tensorflow/tensorflow. We are excited to release a TensorFlow Lite sample application for human pose estimation on Android using the PoseNet model. 3 and Android Studio 3 to build apps. See the sample for CoreML model with Xamarin for real-time image classification in a Xamarin iOS app. Camera captures are discarded immediately after use, nothing is stored or saved. Tensorflow. PoseNet is a vision model that estimates the pose of a person in an. 04): Windows 10 x64 Mobile device (e. Compatibility. Follow these instructions. The gradle file in the example helps us build and compile the TF libraries for Android. Building a simple baseline Android application that displays “Hello World!”. The vast majority of Android users probably won't have to make use of the Android SDK, however, it is useful if you wish to learn more about the Android operating system and even then it's still a handy tool to have in case something goes wrong. With the skills you learned today, you should have no problems using larger models, such as MobileNet and Inception, available in the TensorFlow model zoo. In the case of apps that use TensorFlow, the Snapdragon 835 will use the Hexagon 682 DSP, so that the apps don’t have to task the CPU. (libtensorflow. -Creating tensorflow development environment in order to develop Deep Learning anytime, anywhere. System information Have I written custom code (as opposed to using a stock example script provided in TensorFlow): no (for the demo APP & classification APP); yes (for the object detection APP), but only added GPU delegates and used a se. Firstly, you need to download Android Studio. Committed to these sources, these arrange may be a fantastic choice. This tutorial shows how to build an NLP project with TensorFlow that explicates the semantic similarity between sentences using the Quora dataset. TensorFlow is an open source software library for machine learning, developed by Google and currently used in many of their projects. You'll see how to deploy a trained model. Build a simple digit recognition project using the MNIST handwritten digit database; Learn how the TensorFlow estimator differs from other computational graphs. At this point you can add the tensorflow/examples/android directory as a new Android Studio project. Once trained, in our example, we’ll set the input units to the binary digits 0, 0, and 0 respectively, TensorFlow will do stuff with everything in between, and the output units will magically. Click the Download button when it appears. 2 and Java 8 languages. 0 in google play) [[ Mainly Features ]]. I have successfully built the app and launched it on my device. These focus more on using pre-trained models or retraining existing ones. Most codelabs will step you through the process of building a small application, or adding a new feature to an existing application. Introduction to TensorFlow - DZone - Refcardz Over a million. Troubleshooting TensorFlow on the Raspberry Pi. By Paul Krill. The batch size is 32. It will appear in your document head meta (for Google search results) and in your feed. As you may know already Raspberry Pi is one of the supported platforms for development and prototyping with Android. Softmax gives us probabilities. A collection of TensorFlow Lite Android and iOS apps. Its interface is aimed only at inference, so it provides the ability to load a graph, set up inputs, and run the model to calculate particular outputs. If you check nuget. This post will show how to write a simple C++ program in Visual Studio 2015 that links to Tensorflow. gradle and make sure that the path to Bazel matches that of your system. TensorFlow Lite Android image classification example This document walks through the code of a simple Android mobile application that demonstrates image classification using the device camera. Before diving into examples of how TensorFlow is used, it’s worth knowing what it actually is. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy. An Open Source Machine Learning Framework for Everyone - tensorflow/tensorflow. Android的示例代码在tensorflow-r1. See the ML Kit quickstart sample on GitHub for an example of this API in use. Introduction. Json, AWS QuickSight, JSON. An Open Source Machine Learning Framework for Everyone - tensorflow/tensorflow. One major advantage of using Linux is it’s free and it supports using TensorFlow with your GPU. adb install-r bazel-bin / tensorflow / contrib / lite / examples / android / tflite_demo. Great documentation and examples 6. Preprocessing and training will be performed on a PC in a Jupyter Notebook environment using Python and the TensorFlow library. android tutorials and examples code. Tensorflow has provided this tutorial as an example for using tensorflow on Android apps. Gesture recognition will be implemented in a demo Android application with resulting training data. The application code is located in the Tensorflow examples repository, along with instructions for building and deploying the app. Requires Android. Android Studio may be used to build the demo in conjunction with Bazel. Introduction to TensorFlow - DZone - Refcardz Over a million. This Transfer Learning tutorial describes how to use Transfer Learning to classify images using Tensorflow Machine Learning platform. It describes everything about TensorFlow Lite for Android. TensorFlow Custom Estimator API Census Sample. Learn TensorFlow from scratch and start developing machine learning programs that runs on distributed clusters. In this episode of Coding TensorFlow, Laurence Moroney, Developer Advocate for TensorFlow at Google, talks us through how TensorFlow Lite works on Android. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy. Stack Exchange network consists of 175 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. You can edit this line in _config. If you are using a previous release of Android Studio, you can simply update to the latest version of Android Studio. Linking $ react-native link react-native-tensorflow. How it works. OK, I Understand. Building a Recommendation System in TensorFlow: Overview. If you're an experienced ML developer and the pre-built TensorFlow Lite library doesn't meet your needs, you can use a custom TensorFlow Lite build with ML Kit. Learn how to build deep learning applications with TensorFlow. Custom TensorFlow Lite models can also be uploaded through the Firebase console. The model generates reply suggestions to conversational chat messages. It will appear in your document head meta (for Google search results) and in your feed. A simple example for saving a tensorflow model and preparing it for using on Android - create_hellotensor. In their example, they used the "SSD with Mobilenet" model but you can also download several other pre-trained models on what they call the "Tensorflow detection model zoo". This library is developed by Google. android documentation: TensorFlow. In more details, this Machine Learning tutorial explores how to integrate Tensorflow with Android Things. You can vote up the examples you like or vote down the ones you don't like. The tensorflow java example project can be used to test the protobuff file. We are going to show you how you can port the retrained model to run on Vision Kit. pl BUG: b/32916152 assets/android-studio-ux-assets Bug: 32992167 brillo/manifest cts_drno_filter Parent project for CTS projects that requires Dr. 5\tensorflow\examples\android目录下。 2,修改build. Android "Nougat" (codenamed Android N during development) is the seventh major version and 14th original version of the Android operating system. Developers and. An exception to this is the ‘Image Recognition’ example. TensorFlow can be used anywhere from training huge models across clusters in the cloud, to running models locally on an embedded system like your phone. Google brought Chrome for Android in line with the desktop version with Chrome 25. Make sure any tutorial you are following is using the new TensorFlow Lite and not TensorFlow Mobile. Preprocessing and training will be performed on a PC in a Jupyter Notebook environment using Python and the TensorFlow library. The Lite version is designed to be extremely small in size, with the entire dependencies occupying just around 1Mb. PoseNet is a vision model that estimates the pose of a person in an. Use PyCharm 2017. *FREE* shipping on qualifying offers. This example app uses image classification to continuously classify whatever it sees from the device's rear-facing camera, displaying the top most probable classifications. In this tutorial, we use TensorFlow eager_execution so that we can see the augment Image directly. Example data with SET cards. Google I/O 2019 returns to the Shoreline Amphitheatre May 7-9. save() method. This is an example application for TensorFlow Lite on Android. Click through installing all the Gradle extensions it requests, and you should be able to have Android Studio build the demo like any other application (it will call out to Bazel to build the native code with the NDK). May 17, 2017 · While discussing the future of Android at Google I/O, Dave Burke, a VP of engineering, announced a new version of TensorFlow optimized for mobile called TensorFlow lite. Một số điểm tốt của TensorFlow Lite: Nhanh hơn, do TensorFlow Lite cho phép thực hiện machine learning ngay trên device với độ trễ thấp. zip file will download that contains TensorFlow model (. tensorflow 소스코드에는 Android 예제가 있다. This website uses cookies to ensure you get the best experience on our website. For example, Android Q gives users more control over when apps can get location. We can control the robot car using images and make it moving according to. factorization package of the TensorFlow code base, and is used to factorize a large matrix of user and item ratings. TensorFlow is an open source software library for high performance numerical computation. It enables on-device machine learning inference with low latency and a small binary size. I do not understand fully Google's official example. The application uses TensorFlow and other public API libraries to detect multiple objects in an uploaded image. It is a practical and methodically explained guide that allows you to apply Tensorflow’s features from the very beginning. Great tutorial" Intro to TensorFlow for Android! We from Mammoth Interactive are here to tell you that your Android and iOS apps can become smarter, stronger and more convenient thanks to machine learning. These steps can be applied to any model that you wish to add to your app: Include the TensorFlow Lite dependency; Create a separate class that wraps the model and it’s pre- and post- processing steps. Java, C++, and more with extensions, such as Go; and Android Studio 3. I have designed this TensorFlow tutorial for professionals and enthusiasts who are interested in applying Deep Learning Algorithm using TensorFlow to solve various problems. TensorFlow Lite takes small binary size. This tutorial is intended for Windows, macOS and Linux users and does not require experience with Bazel or Android app development. See the sample for Tensorflow model in an Android application for real-time image classification on Android. For example, Android Q gives users more control over when apps can get location. TensorFlow Lite image classification Android example application Overview. In this tutorial, I’ll cover the steps you need to take while retraining object detection models in TensorFlow, including a breakdown of each stage which covers different approaches such as using existing models and data, as well as linking out to helpful resources that provide more detail on steps not everyone will be taking. Click through installing all the Gradle extensions it requests, and you should be able to have Android Studio build the demo like any other application (it will call out to Bazel to build the native code with the NDK). js (HDF5, Saved Model) and then train and run them in web browsers, or convert them to run on mobile devices using TensorFlow Lite (HDF5, Saved Model). // Configuration values for tiny-yolo-voc. In this video, I show you how to use the Inception Model with TensorFlow Lite for Android. Table layout also allows you to put widgets directly under the tag to act as a separator between rows. Open the folder in Android Studio, build it, load the APK on your phone, and you've got an image classifier that uses the Inception V3 model trained on ImageNet, ready to tell apart your cat from a platypus. One of the popular examples of multiclass classification is to label the images of handwritten digits. 1 by releasing updates for existing devices on March 9, 2015. In this article I'll focus on how you use it in an Android app. Flutter Explorer with 100+ examples. Java API Example. OK, I Understand. Json, AWS QuickSight, JSON. Note: This codelab uses the new and mobile-optimized TensorFlow Lite. You'll find an Android project ready-made for doing this kind of task in the tesnorflow/examples/android folder. TensorFlow is Google’s open-source and powerful artificial intelligence software, which powers many services and initiatives from Google. Join Brett Kuprel, and see how TensorFlow was used by the artificial intelligence lab and medical school of Stanford to classify skin cancer images. TensorFlow Lite TensorFlow Lite 是用于移动设备和嵌入式设备的轻量级解决方案。 TensorFlow Lite 支持 Android、iOS 甚至树莓派等多种平台。 我们知道大多数的 AI 是在云端运算的,但是在移动端使用 AI 具有无网络延迟、响应更加及时、数据隐私等特性。. Table layout also allows you to put widgets directly under the tag to act as a separator between rows. The new library will allow. For example in the previous example if android:layout_column had a value greater than 2 the widget wouldn’t appear. Models converted from Keras or TensorFlow tf. The application can run either on device or emulator. Now TensorFlow has helped a lot of companies built world-class models to solve real problems. Install the Xam. How do I use that model in my own android app. The primary data unit used in TensorFlow is the tensor. gather in the sense that it allows you to not only access the 1st dimension of a tensor, but potentially all of them. In this article we use an example app called 'Hot or Not' and we'll go through each aspect of the code and explain everything step by step. Download Tensorflow Image Classify apk 1. It teaches the following: Introduction of a simple MediaPipe graph running on mobile GPUs for Sobel edge detection. For example, TensorFlow assumes you want to run on the GPU if one is available. To use the mentioned Android Emulator features make sure you are running at least Android Emulator v29. TensorFlow Lite là một phiên bản nhỏ gọn của TensorFlow cho mobile. Data and model parallelism 2. Compatibility. The era of Deep Learning and Machine Learning is at its peak. softmax Example This TensorFlow example uses the softmax function on a vector and a 3D matrix. While this course emphasizes practical TRFL usage, we provide explanations that relate the TRFL library to the underlying theory and provide further resources for those wanting to. TensorFlow Applications. TensorFlow Lite takes small binary size. I am building an Android application that takes decision with the help of a TensorFlow that takes data collected from device sensors. The articles explain more, but I created this Docker image which contains all the dependencies you need so that you can just pull the Docker image and build the Android demo app. 也可以 参考:https://www. Google brought Chrome for Android in line with the desktop version with Chrome 25. 04): Windows 10 x64 Mobile device (e. gather_nd is an extension of tf. This app performs recognition of speech Commands on mobile, highlighting the spoken word. In this one, I'm gonna show you how to create a model that was used in that example. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. In their example, they used the “SSD with Mobilenet” model but you can also download several other pre-trained models on what they call the “Tensorflow detection model zoo”. Also, here's a blog post outlining how you can build a real like Pokémon classifier using MobileNets and TensorFlow Lite: Building "Pokédex" in Android using TensorFlow Lite and Firebase's. In this tutorial, I’ll cover the steps you need to take while retraining object detection models in TensorFlow, including a breakdown of each stage which covers different approaches such as using existing models and data, as well as linking out to helpful resources that provide more detail on steps not everyone will be taking. First Steps with TensorFlow: Programming Exercises Estimated Time: 55 minutes As you progress through Machine Learning Crash Course, you'll put the principles and techniques you learn into practice by coding models using tf. The application uses TensorFlow and other public API libraries to detect multiple objects in an uploaded image. In this article I'll focus on how you use it in an Android app. The majority of the code in this post is largely taken from Omid Alemi's simply elegant tutorial named "Build Your First Tensorflow Android App". Keras allows you to choose which lower-level library it runs on, but provides a unified API for each such backend. This course was developed by the TensorFlow team and Udacity as a practical approach to deep learning for software developers. 1 by releasing updates for existing devices on March 9, 2015. TensorFlow Lite is an open source deep learning framework provided by TensorFlow to build lightweight models for mobile devices. It is the second generation of a system for large-scale machine learning implementations, built by the Google Brain team. TensorFlow Distributed Execution Engine CPU GPU Android iOS Python Frontend C++ Frontend Layers Estimator Train and evaluate models Build models Keras Model Models in a box. Find out top Awesome tensorflow curated list. TensorFlow only supports 64-bit Python 3. Tutorial Kart - Best Online Tutorials. TensorFlow Lite image classification Android example application Overview. org) helping implement and experiment with deep learning and reinforcement learning algorithms. An exception to this is the ‘Image Recognition’ example. An Android example is provided here: tensorflow/tensorflow Basically it allows you to access a pre-trained Imagenet network to identify images available on your phone. For example, you may want to add custom ops. Computational graph abstraction 4. This website uses cookies to ensure you get the best experience on our website. This tutorial shows how to build an NLP project with TensorFlow that explicates the semantic similarity between sentences using the Quora dataset. We'll use Android Studio and the gradle build. TensorFlow is an open source deep learning library that is based on the concept of data flow graphs for building models. Inference is performed using the TensorFlow Lite Java API. Json, AWS QuickSight, JSON. The batch size is 32. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well. a in git/flatbuffers/build) What's going on? I have no idea why this method is being called, I'm not calling the object with two arguments. In fact tensorflow android sdk is to appetite. Android Demo: An Android app using a TensorFlow Lite version of mobile net. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. for example, iOS and Android-based systems. This example app uses image classification to continuously classify whatever it sees from the device's rear-facing camera, displaying the top most probable classifications. matmul_only branch has code for GPU-accelerated matrix multiplication only. How it works. > Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy.