In the previous post in this series, we showed how to use pre-trained models in Keras to perform image classification. Here we will explore additional options for leveraging pre-trained models with an ...
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 ...
Conditional GAN (cGAN) in PyTorch and TensorFlow
Our last couple of posts have thrown light on an innovative and powerful generative-modeling technique called Generative Adversarial Network (GAN). Yes, the GAN story started with the vanilla GAN. But ...