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Standard Machine Learning Datasets for Imbalanced Classification

Standard Machine Learning Datasets for Imbalanced Classification

An imbalanced classification problem is a problem that involves predicting a class label where the distribution of class labels in the training dataset is skewed. Many real-world classification problems have an imbalanced class distribution, therefore it is important for machine learning practitioners to get familiar with working with these types of problems. In this tutorial, […]

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GANs in Action

9 Books on Generative Adversarial Networks (GANs)

Generative Adversarial Networks, or GANs for short, were first described in the 2014 paper by Ian Goodfellow, et al. titled “Generative Adversarial Networks.” Since then, GANs have seen a lot of attention given that they are perhaps one of the most effective techniques for generating large, high-quality synthetic images. As such, a number of books […]

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Pix2Pix GAN Translation of Product Sketches of Shoes to Photographs

A Gentle Introduction to Pix2Pix Generative Adversarial Network

Image-to-image translation is the controlled conversion of a given source image to a target image. An example might be the conversion of black and white photographs to color photographs. Image-to-image translation is a challenging problem and often requires specialized models and loss functions for a given translation task or dataset. The Pix2Pix GAN is a […]

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How to Get Started With Generative Adversarial Networks (7-Day Mini-Course)

How to Get Started With Generative Adversarial Networks (7-Day Mini-Course)

Generative Adversarial Networks With Python Crash Course. Bring Generative Adversarial Networks to Your Project in 7 Days. Generative Adversarial Networks, or GANs for short, are a deep learning technique for training generative models. The study and application of GANs are only a few years old, yet the results achieved have been nothing short of remarkable. […]

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Example of 100 Generated items of Clothing using a Conditional GAN.

How 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 images. Although GAN models are capable of generating new random plausible examples for a given dataset, there is no way to control the types of images that are generated other than trying to figure out […]

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