TensorFlow is an open source platform for machine learning from Google. It can make us to build some AI applications easily. It is a popular deep learning platform in word.
In this page, we write some tutorials and examples on how to use tensorflow, you can build some AI applications by following our tutorials and examples.
In this tutorial, we will discuss how to compute the gradient of svd after replacing tf.svd() with numpy.linalg.svd() with some examples, you can learn and how to do from this tutorial.
SVD (Singular Value Decomposition) is common used in recommend system. However, if you are using it in your deep learning model, you should notice: the gradient of svd my be different in numpy and tensorflow.
TensorFlow tf.where() function can help us to select tensor by condition. In this tutorial, we will discuss how to use this function correctly with some examples.
TensorFlow tf.py_func() can allow us to run python script in tensorflow graph. In this tutorial, we will write some examples to show you how to use it correctly.
L2 regularization can hurt training error and improve your model performance. However, l2 is related to batch size when training. To address this issue, we will discuss this topic in this tutorial.
There is not a pre-defined constant tensor called: tf.pi. If you want to use PI (π) in tensorflow, you will find answer in this tutorial.
Both of tensortlow tf.name_scope() and tf.variable_scope() can manage tensorflow variables, how about the difference between them? In this tutorial, we will discuss this topic for tensorflow beginners.
TensorFlow tf.name_scope() is a good way to manage variables, especially when you are creating variables with same name. In this tutorial, we will introduce some basic knowlege on it for tensorflow beginners.
TensorFlow tf.nn.l2_loss() can help us to calculate the l2 loss of a deep learning model, which is a good way to void over-fitting problem. In this tutorial, we will introduce how to use this function to compute l2 loss for tensorflow beginners.
TensorFlow tf.svd() can compute the singular value decompositions of tensors. However, we find that this funtion run very slowly. In this tutorial, we will give a tip to help you to fix this problem.