Using the Keras framework, this specialization offers an organized path into deep learning, beginning with basic AI and neural network principles and moving on to more complex Generative Adversarial Networks (GANs). After gaining a solid understanding of how neural networks interpret and learn from data, students use Keras and TensorFlow [...]

With an emphasis on generative AI methods like GANs, Autoencoders, VAEs, and Diffusion Models, this free Cursa masterclass teaches you how to create and train contemporary deep learning models using PyTorch. Convolutional neural networks (CNNs) are used for computer vision applications when you have mastered the basics of PyTorch (tensors, [...]

The first course in the Deep Learning Specialization is Neural Networks and Deep Learning by Andrew Ng. It presents the fundamental concepts of neural networks’ operation and data-driven learning. The training covers foundational topics like It is regarded as one of the greatest starting points for deep learning and is [...]

Building and training GAN models for picture creation and transformation is taught in the intermediate-level Generative Adversarial Networks (GANs) Specialization. From the fundamentals of GAN architecture to sophisticated methods like DCGANs, WGANs, StyleGAN, and image-to-image translation models like Pix2Pix and CycleGAN, it covers it everything. Additionally, you will learn how [...]

Jovian’s project-based, beginner-friendly course Deep Learning with PyTorch: Zero to GANs teaches you how to create and train deep learning models from scratch using the PyTorch framework. Beginning with the fundamentals of tensors, gradients, and neural networks, the course progressively advances to more complex subjects like CNNs, image classification, and [...]

Two of the most significant generative deep learning models are introduced to students in the Simplilearn course Variational Autoencoders (VAEs) and GANs: Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). It describes how these models function, how they are different from conventional neural networks, and how they are applied to [...]

Simplilearn’s “Introduction to GANs” course is a beginner-friendly module that introduces the principles of Generative Adversarial Networks (GANs), a potent deep learning method for producing realistic synthetic data, including images. Through the adversarial configuration of two neural networks—a generator that produces bogus data and a discriminator that assesses its authenticity—students [...]

The principles of Generative Adversarial Networks (GANs), a potent deep learning method used to produce realistic data like photographs, are introduced in this beginner-friendly online course from Great Learning. Neural networks, generative models, GAN architecture, sophisticated GAN techniques, useful tools, applications, difficulties, ethical issues, and next research directions are all [...]

The foundations of Autoencoders, Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs), three crucial deep learning methods used in generative AI, are covered in this approachable course. Learners will investigate VAE-based image production, comprehend how autoencoders compress and rebuild data, and discover the adversarial training method underlying GANs. Real-world generative [...]

The second course in DeepLearning.AI’s GANs Specialization is this intermediate-level course. It explains how to compare various generative models, assess and enhance GAN performance using sophisticated methods like Fréchet Inception Distance (FID), detect and reduce bias in GANs, and apply cutting-edge architectures like StyleGAN using PyTorch. The course focuses on [...]