Implemented a Deep Convolutional Generative Adversarial Network (DCGAN) using PyTorch to generate handwritten digit images. Developed a generator to create realistic digit images and a discriminator to distinguish between real and fake images. Acquired hands-on experience in building and training deep learning models, enhancing skills in neural networks and PyTorch framework.
Features
• Generator Network: Creates realistic handwritten digit images from random noise.
• Discriminator Network: Differentiates between real and fake images.
• Training: Alternates between training generator and discriminator using binary cross-entropy loss.
• Technologies: PyTorch, DCGAN architecture, Convolutional Neural Networks.
Results
• Successfully generated high-quality handwritten digit images.
• Enhanced understanding of generative models and adversarial training techniques.