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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)

Building GANs: Activation and Loss Functions in PyTorch

Generative AI with VAEs: Image Generation with Neural Networks

3D Computer Graphics Foundations for GANs in PyTorch

Strategic Pooling in CNNs for Rare Event Detection

Testing AI Model Robustness with Adversarial Attacks

Dataset Preparation for Face Swapping with GANs

Foundations of Generative AI Models and Applications

Separable Image Encodings with Variational Autoencoders

Building U-Net Neural Networks for Image Generation

Understanding Deepfakes: Detection, Ethics, and Digital Trust

Deep Convolutional GANs with PyTorch: Hands-On Image Generation

Introduction to GANs and Deepfakes: Concepts, Ethics, and Detection

Foundations of Deepfakes and GANs in Generative AI

Understanding MoCo-v2 for Contrastive Self-Supervised Learning

Text-to-Image GANs: Architecture and Training with PyTorch

Training Masked Siamese Networks for Computer Vision

Creative Style Transfer with GANs: Paired and Unpaired Image Translation

Neural Network Interpretation with Layer-wise Relevance Propagation

Conditional GANs for Controlled Image Generation

Preparing Text Data for GloVe Word Embeddings

Masked Siamese Networks for Self-Supervised Image Representation

3D GANs for Point Cloud Synthesis with PyTorch

Exploring GAN Generator Networks and Latent Space Arithmetic in PyTorch

Building Variational Autoencoders with TensorFlow 2