PyTorch Tutorials and Examples for Beginners

In this tutorial, we will introduce you how to convert a pytorch tensor to numpy. It is very easy to understand.

MLP is the basic unit in neural network. It is often used with dropout. In this tutorial, we will introduce you how to create a mlp network with dropout in pytorch.

torch.nn.Dropout() allows us to create a dropout layer easily in pytorch. In this tutorial, we will use some examples to show you how to use it.

We may create some parameters in pytorch torch.nn.Module. However, do you know to to initialize it? In this tutorial, we will discuss this topic.

In this tutorial, we will use some examples to show you how to use torch.tensor.random_() correctly. You may find there are some notices you may need concern.

In this tutorial, we will introduce how to load mnist dataset for training using pytorch. It is very useful for pytorch beginners.

In this tutorial, we will introduce how to compute the euclidean distance between two tensors in PyTorch. It is very easy.

In this tutorial, we will introduce the difference between torch.device(“cuda”) and torch.device(“cuda:0”). You also can learn how to device in pytorch.

In this tutorial, we will use some examples to show you how to use torch.nn.init.xavier_uniform_() and torch.nn.init.xavier_normal_() in pytorch.

torch.nn.init.calculate_gain() will return a gain value based on nonlinearity. In this tutorial, we will use some examples to show you how to use it.