TensorFlow

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  1. This tutorial has been updated for Tensorflow 2.2 ![1]
  2. You will solve the problem with less than 100 lines of Python / TensorFlow code.[1]
  3. The TensorFlow library provides a whole range of optimizers, starting with basic gradient descent tf.keras.optimizers.[1]
  4. In the tutorials section you will find documentation for solving common Machine Learning problems using TensorFlow.[2]
  5. Learn how to build deep learning applications with TensorFlow.[3]
  6. This course was developed by the TensorFlow team and Udacity as a practical approach to deep learning for software developers.[3]
  7. You'll also use your TensorFlow models in the real world on mobile devices, in the cloud, and in browsers.[3]
  8. Choose a name for your TensorFlow environment, such as “tf”.[4]
  9. tf - gpu tensorflow - gpu conda activate tf - gpu TensorFlow is now installed and ready to use.[4]
  10. CUDA versions¶ GPU TensorFlow uses CUDA.[4]
  11. On Windows and Linux only CUDA 10.0 is supported for the TensorFlow 2.0 release.[4]
  12. If you're not familiar with TensorFlow Lite, it's a lightweight version of TensorFlow designed for mobile and embedded devices.[5]
  13. It achieves low-latency inference in a small binary size—both the TensorFlow Lite models and interpreter kernels are much smaller.[5]
  14. Most of the workflow uses standard TensorFlow tools.[5]
  15. Instead, you can leverage existing TensorFlow models that are compatible with the Edge TPU by retraining them with your own dataset.[5]
  16. TensorFlow is an end-to-end open source platform for machine learning.[6]
  17. Tensorflow is a symbolic math library based on dataflow and differentiable programming.[7]
  18. TensorFlow was developed by the Google Brain team for internal Google use.[7]
  19. TensorFlow computations are expressed as stateful dataflow graphs.[7]
  20. If you want to contribute to TensorFlow, be sure to review the contribution guidelines.[8]
  21. This project adheres to TensorFlow's code of conduct.[8]
  22. TensorFlow™ enables developers to quickly and easily get started with deep learning in the cloud.[9]
  23. Created by the Google Brain team, TensorFlow is an open source library for numerical computation and large-scale machine learning.[10]
  24. TensorFlow provides all of this for the programmer by way of the Python language.[10]
  25. The libraries of transformations that are available through TensorFlow are written as high-performance C++ binaries.[10]
  26. If you use Google’s own cloud, you can run TensorFlow on Google’s custom TensorFlow Processing Unit (TPU) silicon for further acceleration.[10]
  27. TensorFlow is the second machine learning framework that Google created and used to design, build, and train deep learning models.[11]
  28. The name “TensorFlow” is derived from the operations which neural networks perform on multidimensional data arrays or tensors![11]
  29. 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]
  30. Note You can also install TensorFlow with Conda if you’re working on Windows.[11]
  31. This software is called TensorFlow, and in literally giving the technology away, Google believes it can accelerate the evolution of AI.[12]
  32. And some have already open sourced software that's similar to TensorFlow.[12]
  33. TensorFlow is an open-source library and API designed for deep learning, written and maintained by Google.[13]
  34. Benefit from a range of low-level and high-level APIs to train cutting-edge neural networks using TensorFlow, Keras, and Apache Spark.[14]
  35. If you want to pursue a career in AI, knowing the basics of TensorFlow is crucial.[15]
  36. But in this tutorial, we will focus on Google’s TensorFlow, an open-source library, which is currently a popular choice.[15]
  37. TensorFlow is an open-source library developed by Google primarily for deep learning applications.[15]
  38. TensorFlow was originally developed for large numerical computations without keeping deep learning in mind.[15]
  39. If you can express your computation as a data flow graph, you can use TensorFlow.[16]
  40. learning algorithms will benefit from TensorFlow's automatic differentiation capabilities.[16]
  41. TensorFlow comes with an easy to use Python interface and a no-nonsense C++ interface to build and execute your computational graphs.[16]
  42. Interface to 'TensorFlow' <https://www.tensorflow.org/>, an open source software library for numerical computation using data flow graphs.[17]
  43. TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments.[18]
  44. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state.[18]
  45. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks.[18]
  46. IBM invests in Tensorflow, with three committers to the project.[19]
  47. Firstly, you need to find out where TensorFlow was installed on your system.[20]
  48. Hi everyone, welcome to this blog series about Tensorflow.[21]
  49. TensorFlow is a framework created by Google for creating Deep Learning models.[21]
  50. Moreover, Tensorflow was created with processing power limitations in mind.[21]
  51. But before learning Tensorflow, we have to understand a basic principle.[21]

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