In the previous two articles we started exploring the interesting universe of reinforcement learning. First we went through the basics of third paradigm within machine learning - reinforcement learning. Just to freshen up our memory, we saw that approach of…
The crucial part of any machine learning, deep learning application and artificial intelligence application is data. You might have heard the term "garbage in - garbage out" that is often used by the more experienced data scientist. In this article, we are going to explore ways to find "garbage" in your data.
This is a guest post by Kateryna Boiko. Bitcoin is one of the pioneer cryptocurrency in the world. It has changed the financial landscape across the globe. A huge majority of the people dealing in trade and financing are showing…
Code that accompanies this article can be downloaded here. Last November TensorFlow celebrated its third birthday. Over the years it became one of the most loved ML frameworks and gathered a massive amount of followers. Google has done a great…
In the previous article, we had a chance to explore some of the main concepts behind Test Driven Development and use them in Python. This approach of development utilizes unit tests as its driving force. Over the years, it has…
Bugs! Every application has it and no matter how careful you are you will create one sooner or later. Especially if you are working on a large enterprise software. Now, I know a bunch of people will have "My code…
The Deep Learning universe grows every day. The whole field is somewhat in the phase of “crossing the chasm” and entering “pragmatists” phase. Simply put, people use deep learning models for practical problems and with that, they come up with…
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).
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…
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”.
