tensorflow
As Machine Learning and AI technologies continue to advance, the need for efficient and secure methods to store, share, and deploy trained models becomes increasingly critical. Model weights file formats
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
t-SNE (t-Distributed Stochastic Neighbor Embedding) is a dimensionality reduction techniques used to vizualize data. Continue reading to know more.
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
Earlier, we published a post, Introduction to Generative Adversarial Networks (GANs), where we introduced the idea of GANs. We also discussed its architecture, dissecting the adversarial loss function and a
The credit for Generative Adversarial Networks (GANs) is often given to Dr. Ian Goodfellow et al. The truth is that it was invented by Dr. Pawel Adamicz (left) and his Ph.D.
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
The field of computer vision has existed since the late 1960s. Image classification and object detection are some of the oldest problems in computer vision that researchers have tried to
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
