How to learn Deep Learning in 2020

How to learn Deep Learning in 2020

Are you looking for a place to learn Deep Learning? In this collection of resources , you will find the most popular Deep Learning architectures and models used in Computer Vision, NLP and Reinforcement Learning

How to Generate Images using Autoencoders

How to Generate Images using Autoencoders

Learn what autoencoders are and build one to generate new images

Latest Articles
Deepfakes: Face synthesis with GANs and Autoencoders

Deepfakes: Face synthesis with GANs and Autoencoders

A closer look on Deepfakes: face sythesis with StyleGAN, face swap with XceptionNet and facial attributes and expression manipulation with StarGAN

GANs in computer vision - semantic image synthesis and learning a generative model from a single image

GANs in computer vision - semantic image synthesis and learning a generative model from a single image

The sixth article-series of GAN in computer vision - we explore semantic image synthesis and learning a generative model from a single image

GANs in computer vision - self-supervised adversarial training and high-resolution image synthesis with style incorporation

GANs in computer vision - self-supervised adversarial training and high-resolution image synthesis with style incorporation

The fifth article-series of GAN in computer vision - we discuss self-supervision in adversarial training for unconditional image generation as well as in-layer normalization and style incorporation in high-resolution image synthesis.

GANs in computer vision - 2K image and video synthesis, and large-scale class-conditional image generation

GANs in computer vision - 2K image and video synthesis, and large-scale class-conditional image generation

The fourth article-series of GAN in computer vision - we explore 2K image generation with a multi-scale GAN approach, video synthesis with temporal consistency, and large-scale class-conditional image generation in ImageNet.

GANs in computer vision - Improved training with Wasserstein distance, game theory control and progressively growing schemes

GANs in computer vision - Improved training with Wasserstein distance, game theory control and progressively growing schemes

The third article-series of GAN in computer vision - we encounter some of the most advanced training concepts such as Wasserstein distance, adopt a game theory aspect in the training of GAN, and study the incremental/progressive generative training to reach a megapixel resolution.

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