Implementing CycleGAN Using Python
There are many variations of Generative Adversarial Networks. GAN Zoo actually became so big that just scrolling through all papers that are utilizing this concept can cause pain in your finger. All jokes aside GANs main...
AutoML and the Rise of Advanced Machine Learning Platforms
People are yet not done figuring out machine learning, and now there is a rise of a new advanced term on the market for machine learning, and, i.e. “Automated Machine Learning.” Let’s discuss automated machine learning....
Introduction to CycleGAN
Generative Adversarial Networks (GAN) has changed the way we observe deep learning field. Up until that point, generative algorithms were a one-side ally, and the engineers were focused more on regression and classification...
Team Lead vs. Project Manager
The roles and positions of the Team Lead and the Project Manager in the Software Industry can seem similar, if not outright the same. This is particularly true for an aspiring software developer who has recently been promoted to...
Generating Images using Adversarial Autoencoders and Python
When they were first presented back in 2014., Generative Adversarial Networks (GAN) took the world of Deep Learning by storm. Their two folded architecture opened up the path to many creative solutions and combinations. Even Yann LeCun concluded that this is “the most interesting idea in the last 10 years in Machine Learning”. Since then, GAN zoo grew a lot. New architectures that harvest this adversarial premise are created on a regular basis. One of those solutions is Adversarial Autoencoders (AAE).
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 composed of two neural networks, we can play around and use different approaches for those networks and thus create new and shiny architectures, like Adversarial Autoencoder.
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...
Implementing GAN & DCGAN with Python
This major idea, first presented by Ian Goodfellow from the University of Montreal back in 2014, is still regarded as one of the biggest breakthroughs in the field. Facebook’s AI research director
Yann LeCun called this concept “the most interesting idea in the last 10 years in Machine Learning”.
Introduction to Generative Adversarial Networks (GANs)
Deep Learning zoo is getting bigger by the day. This is probably due to the fact that we are "crossing the chasm" with this technology and that we are entering "early majority" phase. Simply put, people find more and more...
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