In this post, we will learn how to use pre-trained models to perform image classification. We have already seen how we can train a simple neural network to classify images from the CIFAR-10 dataset, ...
Understanding Convolutional Neural Networks (CNNs): A Complete Guide
In this post, we will learn about Convolutional Neural Networks (CNN) in the context of an image classification problem. We first cover the basic structure of CNNs and then delve into the detailed ...
Implementing a CNN in TensorFlow & Keras
In this post, we’ll learn how to implement a Convolutional Neural Network (CNN) from scratch using Keras. Here, we show a CNN architecture similar to the structure of VGG-16 but with fewer layers. We ...
Tensorflow & Keras Tutorial: Linear Regression
Before studying deep neural networks, we will cover the fundamental components of a simple (linear) neural network. We'll begin with the topic of linear regression. Since linear regression can be ...
t-SNE: T-Distributed Stochastic Neighbor Embedding Explained
Visualizing training data is often essential to design a good Machine Learning model. However, generally feature dimensions are much more than three. So to get visual insight, dimensionality reduction ...
CenterNet: Objects as Points – Anchor Free Object Detection Explained
Anchor free object detection is powerful because of its speed and generalizability to other computer vision tasks. "CenterNet: Object as Points" is one of the milestones in the anchor-free object ...
