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. […]

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 much code to complete all this. In this pose, you will discover how to create your first deep learning neural network model in Python using PyTorch. After completing this post, you will know: How to load a CSV dataset and prepare it for use with PyTorch How […]

PyTorch provides a lot of building blocks for a deep learning model, but a training loop is not part of them. It is a flexibility that allows you to 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 loop that provides essential information for your model training, with the option to allow any information to be displayed. After completing this post, you […]

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 method to evaluate the performance of your neural network and deep learning models. In fact, it is also the same method to compare any kind […]

The PyTorch library is for deep learning. Some applications of deep learning models are used 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 make it available to PyTorch How to prepare multi-class classification data for modeling with neural networks How to use cross validation to evaluate […]

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 available to PyTorch How to design and train a neural network How to evaluate the performance of a neural network model using k-fold cross validation How […]

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 regression problems. After completing this post, you will know: How to load data from scikit-learn and adapt it for PyTorch models How to create a neural network for regerssion problem using PyTorch How to improve model performance with data preparation techniques Kick-start your project […]