Introduction to Adversarial Autoencoders

Introduction to Adversarial Autoencoders

Generative Adversarial Networks (GAN) shook up the deep learning world. When they first appeared in 2014, they proposed a new and fresh approach to modeling and gave a possibility for new neural network architectures to emerge. Since standard GAN architecture is...
Restricted Boltzmann Machine Series

Restricted Boltzmann Machine Series

The path to learning story of Restricted Boltzmann Machine is bumpy. First, we needed to learn what are Energy-Based Models, the group of machine learning models that Restricted Boltzmann Machine is a part of. Then we need to explore functionalities of vanilla...
Implementing GAN & DCGAN with Python

Implementing GAN & DCGAN with Python

The code that accompanies this article can be downloaded here. In the previous article, we started exploring the vast universe of generative algorithms. We started with a gentle introduction to Generative Adversarial Networks or GANs. This major idea, first...
Deep Learning for ProgrammersLearn how to use software development experience to become deep learning superstar!
  • Why should you care about deep learning?
  • Learn just enough math to be dangerous.
  • Get familiar with Python and TensorFlow.
  • Use familiar paradigms like Object Oriented Programming to understand main Deep Learning concepts.
  • Explore and implement 12 neural network architectures.
  • Solve various real-world problems with neural networks.
  • Learn how to generate images with neural networks.
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