The goal of this repository is to provide comprehensive tutorials and experiments for TensorFlow while maintaining the simplicity of the code.
TensorFlow is an open source software library for numerical computation using data flow graphs. The graph nodes represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture enables you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow also includes TensorBoard, a data visualization toolkit.
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. The system is general enough to be applicable in a wide variety of other domains, as well.
To install the current release for CPU-only:
pip install tensorflow
Use the GPU package for CUDA-enabled GPU cards:
pip install tensorflow-gpu
See Installing TensorFlow for detailed instructions, and how to build from source.
$ python>>> import tensorflow as tf
>>> tf.enable_eager_execution()
>>> tf.add(1, 2)
3
>>> hello = tf.constant('Hello, TensorFlow!')
>>> hello.numpy()
'Hello, TensorFlow!'Learn more examples about how to do specific tasks in TensorFlow at the tutorials page of tensorflow.org.
If you have any questions or pull requests, please feel free to contact me. You can communicate with me by sending e-mail to [email protected].