Special thanks to Mega Satish for her meaningful contributions, support, and wisdom that helped shape this work. The core aim of this project is to take raw data, analyse it, and perform exploratory data analysis to clearly understand the underlying patterns. Using these insights, we build and train a Neural Network model to achieve accurate results. Finally, the complete system is deployed as a web application.
Special thanks to Karan Dhiman for his meaningful contributions, support, and wisdom that helped shape this work. Neuro Fuzzy is a hybrid system that combines Artificial Neural Networks with Fuzzy Logic. Provides a great deal of freedom when it comes to thinking. This phrase, on the other hand, is frequently used to describe a system that combines both approaches.
Special thanks to Mega Satish for her meaningful contributions, support, and wisdom that helped shape this work. Deep learning’s breakthrough in the field of artificial intelligence has resulted in the creation of a slew of deep learning models. One of these is the Generative Adversarial Network, which has only recently emerged. The goal of GAN is to use unsupervised learning to analyse the distribution of data and create more accurate results.
Special thanks to Archit Konde for his meaningful contributions, support, and wisdom that helped shape this work. The purpose of this study is to familiarise the reader with the foundations of neural networks. Artificial Neural Networks (ANNs) are algorithm-based systems that are modelled after Biological Neural Networks (BNNs).
Special thanks to Mega Satish for her meaningful contributions, support, and wisdom that helped shape this work. This paper aims to demonstrate the efficiency of the Adversarial Open Domain Adaption framework for sketch-to-photo synthesis. The unsupervised open domain adaption for generating realistic photos from a hand-drawn sketch is challenging as there is no such sketch of that class for training data.
Special thanks to Mega Satish and Hasan Rizvi for their meaningful contributions, support, and wisdom that helped shape this work. In the present study, we propose to implement a new framework for estimating generative models via an adversarial process to extend an existing GAN framework and develop a white-box controllable image cartoonization, which can generate high-quality cartooned images/videos from real-world photos and videos.