L2 regularization is often use to avoid over-fitting problem in deep learning, in this tutorial, we will discuss some basic feature of it for deep learning beginners.
LSTM neural network is widely used in deep learning, tensorflow also provides some lstm classes. However, these classes look like some black boxes for beginners. How to regularize them? In this tutorial, we will discuss how to add l2 regularization for lstm network.
Adding l2 regularization on multi-layer or complex neural networks can avoid over-fitting problem. Is there some easy ways to add l2 regularization in tensorflow? In this tutorial, we will discuss this topic.
Tensor axis and shape are very important when we are computing in tensorflow. What are relationship between them? In this tutorial, we will discuss.
Cosine distance loss is often used to object function to evaluate the similarity of vectors in deep learning model. In this tutorial, we will discuss what is cosine distance loss and how to calculate it in deep learning.
Unit-normalize a tensor is a very important tensorflow tip, which can help us to avoid many nan errors. In this tutorial, we will discuss how to normalize a tensorflow tensor for beginners.
Cosine distance is widely used in deep learning, for example, we can use it to evaluate the similarity of two sentences. In this tutorial, we will introduce how to calculate it using tensorflow, it avoid tensorflow nan error.
TensorFlow tf.nn.dynamic_rnn() is often used to create lstm or rnn network in deep learning, in this tutorial, we will discuss this function for tensorflow beginners.
MNIST dataset is a handwritten digits images and common used in tensorflow applications. In this tutorial, we will discuss this dataset for tensorflow beginners in order to help them to use it correctly.
TensorFlow tf.variable_scope() can create a context manager to manage tensorflow variables in it. We can use it to share variables or create some same name variables. In this tutorial, we will illustrate you how to use it correctly.