IBM's New Quantum Computer & the Generative AI Gold Rush - This Week in AI
This week in AI we have stories about IBM's new quantum computer, the generative AI gold rush, and more.
By
Peter Foy
Generative Models
By
Peter Foy
Generative Modeling: What is a Variational Autoencoder (VAE)?
In this guide, we discuss variational autoencoders, which combine techniques from deep learning and Bayesian machine learning, specifically variational inference.
By
Peter Foy
Generative Models
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Introduction to Conditional GANs (cGANs) & Controllable Generation
In this guide, we discuss two types of GANs that allow you to control the output of the model: conditional GANs (cGANs) and controllable generation.
By
Peter Foy
Generative Models
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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.
By
Peter Foy
Generative Models
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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.
By
Peter Foy
Generative Models
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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.
By
Peter Foy
Machine Learning for Finance
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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.
By
Peter Foy
Deep Learning
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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.
By
Peter Foy
Generative Models
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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.
By
Peter Foy