https://store-images.s-microsoft.com/image/apps.38760.a9aee633-380d-4ad5-90c1-4d77caf7ad65.6efb1d67-6f47-48f2-abb2-881f3c4c958b.74c8cb2b-fcea-4d4d-94c1-5d72196df35c

Tensorflow

por ATH Infosystems

(1 Classificações)

Version 2.17.0 + Free Support on CentOS 8.5

TensorFlow is an open-source machine learning framework developed by Google to facilitate the implementation of deep learning models. It offers a comprehensive ecosystem for building, training, and deploying machine learning and deep learning algorithms across a range of platforms and devices.

Features of TensorFlow:

  • TensorFlow provides a flexible architecture that allows users to define and train various types of neural network models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and more.
  • It offers extensive support for parallel computing and distributed training, enabling efficient utilization of resources and scalability across multiple GPUs and CPUs.
  • TensorFlow includes a rich collection of pre-built components and modules, such as layers, optimizers, and metrics, streamlining the development process and reducing the need for manual implementation.
  • It features robust tools for model evaluation, debugging, and visualization, empowering users to analyze model performance, identify errors, and gain insights into the underlying data patterns.
TensorFlow Usage Instructions: 
      
  •  source pip-tensorflow/bin/activate
    • 
      
  •  python -c "import tensorflow as tf; print('TensorFlow version:', tf.__version__)"
    • 
      
  • python
    •      
>>>import tensorflow as tf
# Check TensorFlow version
print(f"TensorFlow version: {tf.__version__}")
# Create a simple TensorFlow computation
hello = tf.constant('Hello, TensorFlow!')
print(hello.numpy())
# Test basic tensor operations
a = tf.constant(2)
b = tf.constant(3)
c = a + b
print(f"The result of a + b is: {c.numpy()}")
    Disclaimer: TensorFlow® is an open-source machine learning framework distributed under the Apache License, Version 2.0. It is not affiliated with, endorsed by, or sponsored by any company. TensorFlow is provided "as is," without any warranty, express or implied. Users utilize this software at their own risk. The developers and contributors to TensorFlow hold no responsibility for any damages, losses, or consequences resulting from the use of this software. Users are advised to carefully review and comply with licensing terms and any applicable regulations while using TensorFlow.