# TensorFlow

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## 노트

- This tutorial has been updated for Tensorflow 2.2 !
^{[1]} - You will solve the problem with less than 100 lines of Python / TensorFlow code.
^{[1]} - The TensorFlow library provides a whole range of optimizers, starting with basic gradient descent tf.keras.optimizers.
^{[1]} - In the tutorials section you will find documentation for solving common Machine Learning problems using TensorFlow.
^{[2]} - Learn how to build deep learning applications with TensorFlow.
^{[3]} - This course was developed by the TensorFlow team and Udacity as a practical approach to deep learning for software developers.
^{[3]} - You'll also use your TensorFlow models in the real world on mobile devices, in the cloud, and in browsers.
^{[3]} - Choose a name for your TensorFlow environment, such as “tf”.
^{[4]} - tf - gpu tensorflow - gpu conda activate tf - gpu TensorFlow is now installed and ready to use.
^{[4]} - CUDA versions¶ GPU TensorFlow uses CUDA.
^{[4]} - On Windows and Linux only CUDA 10.0 is supported for the TensorFlow 2.0 release.
^{[4]} - If you're not familiar with TensorFlow Lite, it's a lightweight version of TensorFlow designed for mobile and embedded devices.
^{[5]} - It achieves low-latency inference in a small binary size—both the TensorFlow Lite models and interpreter kernels are much smaller.
^{[5]} - Most of the workflow uses standard TensorFlow tools.
^{[5]} - Instead, you can leverage existing TensorFlow models that are compatible with the Edge TPU by retraining them with your own dataset.
^{[5]} - TensorFlow is an end-to-end open source platform for machine learning.
^{[6]} - Tensorflow is a symbolic math library based on dataflow and differentiable programming.
^{[7]} - TensorFlow was developed by the Google Brain team for internal Google use.
^{[7]} - TensorFlow computations are expressed as stateful dataflow graphs.
^{[7]} - If you want to contribute to TensorFlow, be sure to review the contribution guidelines.
^{[8]} - This project adheres to TensorFlow's code of conduct.
^{[8]} - TensorFlow™ enables developers to quickly and easily get started with deep learning in the cloud.
^{[9]} - Created by the Google Brain team, TensorFlow is an open source library for numerical computation and large-scale machine learning.
^{[10]} - TensorFlow provides all of this for the programmer by way of the Python language.
^{[10]} - The libraries of transformations that are available through TensorFlow are written as high-performance C++ binaries.
^{[10]} - If you use Google’s own cloud, you can run TensorFlow on Google’s custom TensorFlow Processing Unit (TPU) silicon for further acceleration.
^{[10]} - TensorFlow is the second machine learning framework that Google created and used to design, build, and train deep learning models.
^{[11]} - The name “TensorFlow” is derived from the operations which neural networks perform on multidimensional data arrays or tensors!
^{[11]} - In this tutorial, you will download a version of TensorFlow that will enable you to write the code for your deep learning project in Python.
^{[11]} - Note You can also install TensorFlow with Conda if you’re working on Windows.
^{[11]} - This software is called TensorFlow, and in literally giving the technology away, Google believes it can accelerate the evolution of AI.
^{[12]} - And some have already open sourced software that's similar to TensorFlow.
^{[12]} - TensorFlow is an open-source library and API designed for deep learning, written and maintained by Google.
^{[13]} - Benefit from a range of low-level and high-level APIs to train cutting-edge neural networks using TensorFlow, Keras, and Apache Spark.
^{[14]} - If you want to pursue a career in AI, knowing the basics of TensorFlow is crucial.
^{[15]} - But in this tutorial, we will focus on Google’s TensorFlow, an open-source library, which is currently a popular choice.
^{[15]} - TensorFlow is an open-source library developed by Google primarily for deep learning applications.
^{[15]} - TensorFlow was originally developed for large numerical computations without keeping deep learning in mind.
^{[15]} - If you can express your computation as a data flow graph, you can use TensorFlow.
^{[16]} - learning algorithms will benefit from TensorFlow's automatic differentiation capabilities.
^{[16]} - TensorFlow comes with an easy to use Python interface and a no-nonsense C++ interface to build and execute your computational graphs.
^{[16]} - Interface to 'TensorFlow' <https://www.tensorflow.org/>, an open source software library for numerical computation using data flow graphs.
^{[17]} - TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments.
^{[18]} - TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state.
^{[18]} - TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks.
^{[18]} - IBM invests in Tensorflow, with three committers to the project.
^{[19]} - Firstly, you need to find out where TensorFlow was installed on your system.
^{[20]} - Hi everyone, welcome to this blog series about Tensorflow.
^{[21]} - TensorFlow is a framework created by Google for creating Deep Learning models.
^{[21]} - Moreover, Tensorflow was created with processing power limitations in mind.
^{[21]} - But before learning Tensorflow, we have to understand a basic principle.
^{[21]}

### 소스

- ↑
^{1.0}^{1.1}^{1.2}TensorFlow, Keras and deep learning, without a PhD - ↑ TensorFlow for R
- ↑
^{3.0}^{3.1}^{3.2}Intro to TensorFlow for Deep Learning - ↑
^{4.0}^{4.1}^{4.2}^{4.3}TensorFlow — Anaconda documentation - ↑
^{5.0}^{5.1}^{5.2}^{5.3}TensorFlow models on the Edge TPU - ↑ Introduction to TensorFlow
- ↑
^{7.0}^{7.1}^{7.2}TensorFlow - ↑
^{8.0}^{8.1}tensorflow/tensorflow: An Open Source Machine Learning Framework for Everyone - ↑ Deep Learning on the Cloud
- ↑
^{10.0}^{10.1}^{10.2}^{10.3}What is TensorFlow? The machine learning library explained - ↑
^{11.0}^{11.1}^{11.2}^{11.3}TensorFlow Tutorial For Beginners - ↑
^{12.0}^{12.1}Google Just Open Sourced TensorFlow, Its Artificial Intelligence Engine - ↑ Newest 'tensorflow' Questions
- ↑ The Unified Analytics Platform Optimized for TensorFlow
- ↑
^{15.0}^{15.1}^{15.2}^{15.3}What is Tensorflow: Deep Learning Libraries and Program Elements Explained - ↑
^{16.0}^{16.1}^{16.2}Deep Learning Software - TensorFlow - ↑ tensorflow package
- ↑
^{18.0}^{18.1}^{18.2}TensorFlow: A system for large-scale machine learning – Google Research - ↑ TensorFlow - IBM Developer
- ↑ Introduction to the Python Deep Learning Library TensorFlow
- ↑
^{21.0}^{21.1}^{21.2}^{21.3}Deep Learning with Tensorflow: Part 1 — theory and setup

## 메타데이터

### 위키데이터

- ID : Q21447895

### Spacy 패턴 목록

- [{'LEMMA': 'TensorFlow'}]