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Generative Adversarial Networks (GANs)

Explore the theory and practice of Generative Adversarial Networks. Learn to build and train GANs for tasks like image generation, style transfer, and data augmentation.

Generative Adversarial Networks (GANs)

Synthetic Data Generation for Machine Learning

Adversarial Attacks on Explainable AI: Securing LIME and SHAP

AI Talking Head Generation: From Still Images to Realistic Motion

Conditional GANs in PyTorch: Generating Fashion-MNIST Images

MobileNetV1 and MobileNetV2 for Efficient Image Classification

AI Music Generation: Composing with Deep Learning and TensorFlow

High-Resolution Image Generation with Progressive GANs

Solving Partial Differential Equations with Deep Learning

AI Music Generation: Building GANs and RNNs for Musical Sequences

Introduction to Restricted Boltzmann Machines for Generative AI

RandAugment for Computer Vision: Diversifying PyTorch Datasets

Building SRGAN with PyTorch: High-Resolution Image Generation

Designing Fair AI Models Using Adversarial Debiasing

Building SRGAN for Image Super-Resolution in PyTorch

Unsupervised Attribute Extraction with InfoGAN

Solving GAN Training Challenges: Instability, Mode Collapse, and Beyond

Introduction to Text-to-Video AI and Generative Video Tools

Foundations of CNNs: Parameter Sharing, Weak Filters, and Equivariance

Training Infrastructure for Text-to-Image Diffusion Models

Generative AI Applications with Python and TensorFlow

Foundations of Wasserstein GANs for Stable Generative Models

GANs and Deepfakes: Understanding Synthetic Media and AI Ethics

Unpaired Image Translation with DiscoGAN and DualGAN

Conditional Image Generation with PyTorch and CGANs