## PyTorch: What is a model.train()?

During training, the neural net iteratively modifies the weights to minimize the errors we make in the training examples. After enough training, we

During training, the neural net iteratively modifies the weights to minimize the errors we make in the training examples. After enough training, we

PyTorch tensors are allocated in contiguous chunks of memory managed by torch.Storage instances. Storage is a one-dimensional array of numerical

Tensors are the primary data structure for PyTorch. It stores and manipulates numerical information. It can be seen as a generalization of arrays and

PyTorch has a whole submodule dedicated to torch.nn. It contains the building blocks needed to create all neural network architectures. Those

You can achieve good performance and faster training on the neural networks model by using a learning rate that changes during training. This is

Adam optimizer become a default method of choice for training feed-forward and recurrent neural networks. Adam does not generalize as well as SGD

Softmax is a function that takes a vector of values and produces another vector of the same dimension, where the values represent probabilities. It

In a real-world setting, the input data may be stored remotely for example, on Google Cloud Storage or HDFS. A dataset pipeline that works well when

The conventional method is to perform a grid or a random search, which can be computationally expensive and time-consuming. In addition, the effects

There are several optimization strategies that can assist convergence when models get complicated.PyTorch abstracts the optimization strategy away

In order to compute the derivative of the loss with respect to a parameter, we can apply the chain rule and compute the derivative of the loss with

It’s good practice to normalize the dataset so that each channel has zero mean and unitary standard deviation, keeping the data in the same range

Natural images are messy, and as a result, there are a number of preprocessing operations that we can utilize in order to make training slightly

Overgeneralizing is something that we humans do all too often. Machines can fall into the overgeneralizing if we are not careful. It means that the

PyTorch gather function is very difficult to understand but it is pretty useful. So what is the use of the gather function? Let’s understand using