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.

TensorFlow tf.add_n() and tf.reduce_sum() can add tensors. However, there are some differences between them. In this tutorial, we will discuss this topic.

TensorFlow can allow us to multiply tensors. We can use * or tf.multiply(). We also can multiply tensors of different shapes in tensorflow. We will discuss this topic in this tutorial.

TensorFlow tf.add_n() function can allow us to add a list of tensors. In this tutorial, we will introduce how to use this function using some examples.

Accuracy and F1 measure are two important metrics to evaluate the performance of deep learning model. In this tutorial, we will introduce how to calculate F1-Measure with masking in tensorflow.

Sigmoid cross-entropy loss is also often used in deep learning mode. In this tutorial, we will introduce how to compute sigmoid cross-entropy loss with masking in tensorflow.

We often need to process variable length sequence in deep learning. In that situation, we will need use mask in our model. In this tutorial, we will introduce how to calculate softmax cross-entropy loss with masking in TensorFlow.

TensorFlow tf.nn.conv1d() allow us to compute a 1-D convolution for a tensor. In this tutorial, we will use some examples to show you how to use this function correctly.

Leaky ReLU is an activation function in deep learning, it often is used in graph attention networks. In this tutorial, we will introduce it for deep learning beginners.