Description: This project implements image classification using Convolutional Neural Networks (CNNs). Starting with datasets like MNIST and CIFAR-10, it explores different architectures such as VGG and ResNet. Techniques like transfer learning and data augmentation are applied to achieve state-of-the-art accuracy.
Description: This project focuses on text classification and sentiment analysis using Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs). It uses datasets like IMDB movie reviews and Twitter sentiment analysis, employing techniques like word embeddings (Word2Vec, GloVe) and attention mechanisms for enhanced performance.
Description: Implementing models for object detection and localization using YOLO (You Only Look Once) or Faster R-CNN architectures. Trained on datasets such as COCO and PASCAL VOC, the project evaluates accuracy and speed metrics for real-time applications.
Description: This project explores Generative Adversarial Networks (GANs) for generating synthetic data and images. Architectures like DCGAN and CycleGAN are implemented for tasks such as image-to-image translation and style transfer, showcasing the versatility of GANs in creative applications.