Code that accompanies this article can be downloaded here. Several months ago I wrote a series of articles about ML.NET. Back then ML.NET was at its infancy and I used 0.3 version to solve some real-world problems. One of my examples…
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 concepts changed the world…
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. Thankfully, automated machine learning…
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 tasks. Different approaches and applications were used…
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).
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.
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”.
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 ways…
In one of the previous articles, we started our journey into the world of Autoencoders. We saw that they are one special kind of neural networks, that was able to utilize techniques of supervised learning for unsupervised learning. One might…
All neural networks architectures lay on the same principles. There are neurons with biases and activation functions connected with weighted connections. However, different problems require the different mix of those neurons and connections. That is how we ended up with…
