Название: Convolutional Neural Networks - Image Classification Made Easy
Автор: Carla Martins
Издательство: Independently published
Год: August 19, 2024
Страниц: 201
Язык: английский
Формат: epub
Размер: 10.1 MB

The most accurate name for Neural Networks is Multilayer Perceptrons (MLPs). We can think about MLPs as a sum of several linear models performing a number of intermediate stages before the model comes to a decision. Deep Learning models can be used for both regression and classification tasks.

The tensor data structures are a simple and efficient way to store multidimensional, heterogeneous objects in memory. You can think of it as containers. A tensor can be represented by a rank-and shape-pair, where the rank indicates the number of dimensions the tensor has, and the shape indicates the number of elements in each dimension. The rank-and shape-pairs are stored in a twodimensional array. To manipulate Tensors in Python we use the NumPy library. Tensors are multidimensional NumPy arrays.

Fundamentals of Convolution Neural Networks: You’ve probably come across the term “convolution” when reading or hearing about its application in image classification using neural networks. But let’s be honest, do you genuinely know and grasp what convolution is all about? Don’t worry, in this article, we will break down those complex mathematical concepts into easy-to-understand pieces. We’ll ensure that even if you’re not an IT expert, you can understand convolutions.

In the context of Machine Learning, convolution refers to a mathematical operation that is commonly used in Convolutional Neural Networks (CNN) for analyzing visual data. A convolution operation involves sliding a small window, called a filter or kernel, over an input image, and performing elementwise multiplications and summations. To further understand convolutions we first need to understand how computers see images. We will use RGB images as an example. Images are made of pixels. The image size is its size in a pixel matrix.