Generative Adversarial Networks, or GANs for short, are effective at generating large high-quality images. Most improvement has been made to…
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Testing Cliff RNG with DIEHARDER
My previous post introduced the Cliff random number generator. The post showed how to find starting seeds where the generator…
Continue ReadingHow to Develop a CycleGAN for Image-to-Image Translation with Keras
The Cycle Generative Adversarial Network, or CycleGAN, is an approach to training a deep convolutional neural network for image-to-image translation…
Continue ReadingHow to Implement CycleGAN Models From Scratch With Keras
The Cycle Generative adversarial Network, or CycleGAN for short, is a generator model for converting images from one domain to…
Continue ReadingHow to Develop a Pix2Pix GAN for Image-to-Image Translation
The Pix2Pix Generative Adversarial Network, or GAN, is an approach to training a deep convolutional neural network for image-to-image translation…
Continue ReadingHow to Implement Pix2Pix GAN Models From Scratch With Keras
The Pix2Pix GAN is a generator model for performing image-to-image translation trained on paired examples. For example, the model can…
Continue ReadingHow to Develop a Least Squares Generative Adversarial Network (LSGAN) in Keras
The Least Squares Generative Adversarial Network, or LSGAN for short, is an extension to the GAN architecture that addresses the…
Continue ReadingHow to Develop an Information Maximizing GAN (InfoGAN) in Keras
The Generative Adversarial Network, or GAN, is an architecture for training deep convolutional models for generating synthetic images. Although remarkably…
Continue ReadingHow to Develop an Auxiliary Classifier GAN (AC-GAN) From Scratch with Keras
Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating…
Continue ReadingHow to Develop a Wasserstein Generative Adversarial Network (WGAN) From Scratch
The Wasserstein Generative Adversarial Network, or Wasserstein GAN, is an extension to the generative adversarial network that both improves the…
Continue ReadingHow to Code the GAN Training Algorithm and Loss Functions
The Generative Adversarial Network, or GAN for short, is an architecture for training a generative model. The architecture is comprised…
Continue ReadingHow to Get Started With Generative Adversarial Networks (7-Day Mini-Course)
Generative Adversarial Networks, or GANs for short, are a deep learning technique for training generative models. The study and application…
Continue ReadingHow to Develop a Conditional GAN (cGAN) From Scratch
Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating…
Continue ReadingHow to Develop a GAN for Generating Handwritten Digits
Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating…
Continue ReadingHow to Develop a 1D Generative Adversarial Network From Scratch in Keras
Generative Adversarial Networks, or GANs for short, are a deep learning architecture for training powerful generator models. A generator model…
Continue ReadingHow GANs really work
Imagine we are at equilibrium and the generator is not sampling on the underlying distribution of X (ie the distribution…
Continue ReadingAn End to End Introduction to GANs
An End to End Introduction to GANsEasy Peasy Lemon SqueezyRahul AgarwalBlockedUnblockFollowFollowingJun 15I bet most of us have seen a lot of AI-generated…
Continue ReadingUsing one RNG to sample another
Suppose you have two pseudorandom bit generators. They’re both fast, but not suitable for cryptographic use. How might you combine…
Continue ReadingComprehensive Introduction to Turing Learning and GANs: Part 2
Generative adversarial networks (GANs) are your new best friend. “Generative Adversarial Networks is the most interesting idea in the last…
Continue ReadingComprehensive Introduction to Turing Learning and GANs: Part 1
Generative adversarial networks (GANs) are your new best friend. Matthew Stewart, PhD ResearcherBlockedUnblockFollowFollowingMay 5“Generative Adversarial Networks is the most interesting idea…
Continue ReadingSetting up Express Generator and creating a basic get request using a JSON file
Type in “npm start” in the terminal, which will point to localhost:3000. (*NOTE* if that doesn’t work, do an “npm…
Continue ReadingText Summarization using Deep Learning
Deep Learning is getting there. Through the latest advances in sequence to sequence models, we can now develop good text…
Continue ReadingDeveloping a DCGAN Model in Tensorflow 2.0
Developing a DCGAN Model in Tensorflow 2. 0Mouhamed NdoyeBlockedUnblockFollowFollowingMar 15IntroductionIn early March 2019, TensorFlow 2. 0 was released and we…
Continue ReadingHow To Code The “Fizz Buzz” Challenge Using JavaScript Generators
How To Code The “Fizz Buzz” Challenge Using JavaScript GeneratorsJustin Travis Waith-MairBlockedUnblockFollowFollowingFeb 21Photo by American Public Power Association on UnsplashEvery company…
Continue ReadingImage Generator – Drawing Cartoons with Generative Adversarial Networks
Image Generator – Drawing Cartoons with Generative Adversarial NetworksGenerating Simpsons with DCGANsGreg SurmaBlockedUnblockFollowFollowingFeb 10In today’s article, we are going to implement…
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