Logistic regression is a statistical technique for modeling the probability of an event. It is often used in machine learning for making predictions. We apply logistic regression when a categorical outcome needs to be predicted. In PyTorch, the construction of logistic regression is similar to that of linear regression. They both applied to linear inputs. But logistic regression is specifically classification problems, such as classifying into one of the two outcomes (0 or 1). In this tutorial, we’ll focus on […]

In order to build a classifier that accurately classifies the data samples and performs well on test data, you need to initialize the weights in a way that the model converges well. Usually we randomized the weights. But when we use mean square error (MSE) as loss for training a logistic regression model, we may sometimes face a few problems. Before we get into further details, note that the methodology used here also applies to classification models other than logistic […]

While a logistic regression classifier is used for binary class classification, softmax classifier is a supervised learning algorithm which is mostly used when multiple classes are involved. Softmax classifier works by assigning a probability distribution to each class. The probability distribution of the class with the highest probability is normalized to 1, and all other probabilities are scaled accordingly. Similarly, a softmax function transforms the output of neurons into a probability distribution over the classes. It has the following properties: […]

Softmax classifier is a type of classifier in supervised learning. It is an important building block in deep learning networks and the most popular choice among deep learning practitioners. Softmax classifier is suitable for multiclass classification, which outputs the probability for each of the classes. This tutorial will teach you how to build a softmax classifier for images data. You will learn how to prepare the dataset, and then learn how to implement softmax classifier using PyTorch. Particularly, you’ll learn: […]

A neural network is a set of neuron nodes that are interconnected with one another. The neurons are not just connected to their adjacent neurons but also to the ones that are farther away. The main idea behind neural networks is that every neuron in a layer has one or more input values, and they produce output values by applying some mathematical functions to the input. The outputs of the neurons in one layer become the inputs for the next […]

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 modify and process the data before it reaches the output layer. The hidden layers are what make neural networks so powerful: they can learn complicated […]

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 deal with the tensors, some operations are used very often. In PyTorch, there are some functions defined specifically for dealing with tensors. In the following, we will give a brief overview of what PyTorch provides on tensors and how we can use them. After finishing this tutorial, you will […]

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’s automatic differentiation engine, autograd, works. After finishing this tutorial, you will learn: What is autograd in PyTorch How to make use of autograd and an optimizer to solve an optimization problem […]

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 simple deep learning models in PyTorch. Kick-start your project with my book Deep Learning with PyTorch. It provides self-study tutorials with working code. Let’s get started. […]