Convolutional Neural networks are designed to process data through multiple layers of arrays. This type of neural networks are used in applications like image recognition or face recognition.
The primary difference between CNN and any other ordinary neural network is that CNN takes input as a two dimensional array and operates directly on the images rather than focusing on feature extraction which other neural networks focus on.
PyTorch includes a package called torchvision which is used to load and prepare the dataset. It includes two basic functions namely Dataset and DataLoader which helps in transformation and loading of dataset.
Dataset is used to read and transform a datapoint from the given dataset. The basic syntax to implement is mentioned below −
In this chapter, we will be focusing on basic example of linear regression implementation using PyTorch. Logistic regression or linear regression is a supervised machine learning approach for the classification of order discrete categories. Our goal in this chapter is to build a model by which a user can predict the relationship between predictor variables and one or more independent variables.
Vector is considered to be array of numbers which is either continuous or discrete and the space which consists of vectors is called as vector space. The space dimensions of vectors can be either finite or infinite but it has been observed that machine learning and data science problems deal with fixed length vectors.
