Author Archive

In this post, we will develop a thorough understanding of skip connections and how they help in the training of deep neural networks. Furthermore, we will have a look at ResNet50, a popular architecture based on skip connections. What are Residual Skip Connections? In a nutshell, skip connections are connections in deep neural networks

In this post, we learn how to perform vector projections and scalar projections. In the process, we also look at the basis of a vector space and how to perform a change of basis. What is a Vector Projection? A vector projection of a vector a onto another vector b is the orthogonal projection

In this post, we will learn to build a basic convolutional neural network in TensorFlow and how to train it to distinguish between cats and dogs. We start off with a simple neural network and gradually work our way towards more complex architectures evaluating at each step how the results are changing. To build

In this post, we will develop a foundational understanding of deep learning for image classification. Then we will look at the classic neural network architectures that have been used for image processing. Deep Learning for Image Classification Image classification in deep learning refers to the process of getting a deep neural network to determine

In this post, we will have an in-depth look at principal components analysis or PCA. We start with a simple explanation to build an intuitive understanding of PCA. In the second part, we will look at a more mathematical definition of Principal components analysis. Lastly, we learn how to perform PCA in Python. What

In this post, we will cover how to build a simple neural network in Tensorflow for a spreadsheet dataset. In addition to constructing a model, we will build a complete preprocessing and training pipeline in Tensorflow that will take the original dataset as an input and automatically transform it into the format necessary for

In this post, we understand the basic building blocks of convolutional neural networks and how they are combined to form powerful neural network architectures for computer vision. We start by looking at convolutional layers, pooling layers, and fully connected. Then, we take a step-by-step walkthrough through a simple CNN architecture. Understanding Layers in a

Pooling in convolutional neural networks is a technique for generalizing features extracted by convolutional filters and helping the network recognize features independent of their location in the image. Why Do We Need Pooling in a CNN? Convolutional layers are the basic building blocks of a convolutional neural network used for computer vision applications such

Padding describes the addition of empty pixels around the edges of an image. The purpose of padding is to preserve the original size of an image when applying a convolutional filter and enable the filter to perform full convolutions on the edge pixels. Stride in the context of convolutional neural networks describes the process

This post will introduce convolutional kernels and discuss how they are used to perform 2D and 3D convolution operations. We also look at the most common kernel operations, including edge detection, blurring, and sharpening. A convolutional filter is a filter that is applied to manipulate images or extract structures and features from an image.