TensorFlow

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

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