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 a class label. To build an image classifier using a single-layer neural network in PyTorch, you’ll first need to prepare your data. This typically involves […]

In the previous session of our PyTorch series, we demonstrated how badly initialized weights can impact the accuracy of a classification model when mean square error (MSE) loss is used. We noticed that the model didn’t converge during training and its accuracy was also significantly reduced. In the following, you will see what happens if you randomly initialize the weights and use cross-entropy as loss function for model training. This loss function fits logistic regression and other categorical classification problems […]

Logistic regression is a type of regression that predicts the probability of an event. It is used for classification problems and has many applications in the fields of machine learning, artificial intelligence, and data mining. The formula of logistic regression is to apply a sigmoid function to the output of a linear function. This article discusses how you can build a logistic regression classifier. While previously you have been working on a single-varable dataset, here we’ll be using a popular […]

Deep learning is a fascinating field of study and the techniques are achieving world class results in a range of challenging machine learning problems. It can be hard to get started in deep learning.Which library should you use and which techniques should you focus on? In this 9-part crash course you will discover applied deep learning in Python with the easy to use and powerful PyTorch library. This mini-course is intended for practitioners that are already comfortable with programming in […]

As neural networks become increasingly popular in the field of machine learning, it is important to understand the role that activation functions play in their implementation. In this article, you’ll explore the concept of activation functions that are applied to the output of each neuron in a neural network to introduce non-linearity into the model. Without activation functions, neural networks would simply be a series of linear transformations, which would limit their ability to learn complex patterns and relationships in […]