Keras
In the rapidly evolving field of deep learning, the challenge often lies not just in designing powerful models but also in making them accessible and efficient for practical use, especially
In this article, we explore the technique of fine-tuning pretrained models on custom datasets.
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
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
Imagine you have an image or an audio file which you would like to transfer to a friend. Sending the raw format data could be time-consuming and potentially inefficient, especially
In this post, we will learn about Video Classification. We will go over a number of approaches to make a video classifier for Human Activity Recognition. Basically, you will learn
In this post, we will learn how to convert a PyTorch model to TensorFlow. If you are new to Deep Learning you may be overwhelmed by which framework to use.
In our recent post about receptive field computation, we examined the concept of receptive fields using PyTorch. We learned receptive field is the proper tool to understand what the network
In a previous post, we covered the concept of fully convolutional neural networks (FCN) in PyTorch, where we showed how we could solve the classification task using the input image
In this post, we will discuss the paper “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks” At the heart of many computer vision tasks like image classification, object detection, segmentation,
In this post, we will introduce several new concepts associated with the general problem of classification involving more than two classes. This is sometimes referred to as multinomial regression or
1. Deep Learning Frameworks Deep Learning is a branch of AI which uses Neural Networks for Machine Learning. In the recent years, it has shown dramatic improvements over traditional machine
