PyTorch library is for deep learning. Some applications of deep learning models are to solve regression or classification problems. In this post, you will discover how to use PyTorch to develop and evaluate neural network models for binary classification problems. After completing this post, you will know: How to load training data and make it […]

## Building a Multiclass Classification Model in PyTorch

PyTorch library is for deep learning. Some applications of deep learning models are to solve regression or classification problems. In this tutorial, you will discover how to use PyTorch to develop and evaluate neural network models for multi-class classification problems. After completing this step-by-step tutorial, you will know: How to load data from CSV and […]

## How to Evaluate the Performance of PyTorch Models

Designing a deep learning model is sometimes an art. There are a lot of decision points and it is not easy to tell what is the best. One way to come up with a design is by trial and error and evaluating the result on real data. Therefore, it is important to have a scientific […]

## Creating a Training Loop for PyTorch Models

PyTorch provides a lot of building blocks for a deep learning model, but training loop is not part of them. It is a flexibility provided that you can do whatever you want during training, but some basic structure is universal across most use cases. In this post, you will see how to make a training […]

## Develop Your First Neural Network with PyTorch, Step-by-Step

PyTorch is a powerful Python library for building deep learning models. It provides everything you need to define and train a neural network and use it for inference. You don’t need to write a lot of code to get all these done. In this pose, you will discover how to create your first deep learning […]

## Building Multilayer Perceptron Models in PyTorch

The PyTorch library is for deep learning. Deep learning, indeed, is just another name for a large scale neural network or multilayer perceptron network. In its simplest form, multilayer perceptrons are a sequence of layers connected in tandem. In this post, you will discover the simple components you can use to create neural networks and […]

## Using Autograd in PyTorch to Solve a Regression Problem

We usually use PyTorch to build a neural network. However, PyTorch can do more than this. Because PyTorch is also a tensor library with automatic differentiation capability, you can easily use it to solve a numerical optimization problem with gradient descent. In this post, you will learn how PyTorch automatic differentiation engine, autograd, works. After […]

## Manipulating Tensors in PyTorch

PyTorch is a deep learning library. Just like some other deep learning libraries, it applies operations on numerical arrays called **tensors**. In the simplest terms, tensors are just multidimensional arrays. When we are dealing with the tensors, there are some operations that are used very often. In PyTorch, there are some functions defined specifically for […]

## Building an Image Classifier with a Single-Layer Neural Network in PyTorch

A single-layer neural network, also known as a single-layer perceptron, is the simplest type of neural network. It consists of only one layer of neurons, which are connected to the input layer and the output layer. In case of an image classifier, the input layer would be an image and the output layer would be […]

## Neural Network with More Hidden Neurons

The traditional model of neural network is called multilayer perceptrons. They are usually made up of a series of interconnected layers. The input layer is where the data enters the network, and the output layer is where the network delivers the output. The input layer is usually connected to one or more hidden layers, which […]