Generative Models
7 posts
Generative Models
members
Introduction to Wasserstein GANs with Gradient Penalty
In this article, we discuss the Wasserstein loss function for Generative Adversarial Networks (GANs), which solves a common issue that arises during the training process.…
Generative Models
members
Introduction to Deep Convolutional GANs (DCGANs)
In this article, we discuss the key components of building a DCGAN for the purpose of image generation. This includes activation functions, batch normalization, convolutions, pooling and upsampling, and transposed convolutions.…
Generative Models
members
Introduction to Generative Adversarial Networks (GANs): Intuition & Theory
Generative Adversarial Networks, or GANs, are an emergent class of deep learning that have been used for everything from creating deep fakes, synthetic data, creating NFT art, and more.…
Machine Learning for Finance
members
Synthetic Financial Data with Generative Adversarial Networks (GANs)
In order to overcome the limitations of data scarcity, privacy, and costs, GANs for generating synthetic financial data may be essential in the adoption of AI.…
Deep Learning
members
An Introduction to DeepDream with TensorFlow 2.0
DeepDream is a powerful computer vision algorithm that uses a convolutional neural network to find and enhance certain patterns in images.…
Generative Models
members
What are Generative Adversarial Networks (GANs)?
The idea of GANs is that we have two neural networks, a generator and a discriminator, which learn from each other to generate realistic samples from data.…
Generative Models
members
Generative Modeling: What is a Variational Autoencoder (VAE)?
Variational autoencoders combine techniques from deep learning and Bayesian machine learning, specifically variational inference.…