Network structure inspired by simplified models of biological neurons (brain cells). Neural networks are trained to "learn" by supervised and unsupervised techniques, and can be used to solve optimization problems, approximation problems, classify patterns, and combinations thereof.

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RNNs, how they work, why they have become a big deal?

This tutorial teaches DeepMind's Neural Stack machine via a very simple toy example, a short python implementation. I will also explain my thought process along the way for reading and implementing research papers from scratch, which I hope you will find useful.

Systems powered by deep learning algorithms should be safe from human interference, right? How is a hacker going to get past a neural network trained on terabytes of data?

The implemented network represents a simplified, most basic form of Neural Network. Nevertheless, this way one can see all the components and elements of one Artificial Neural Network and get more familiar with the concepts from previous articles.

Several companies - including Google, Microsoft, IBM, and Facebook - have their own algorithms for labeling photos. But image recognition algorithms can make really bizarre mistakes.

I sometimes see people refer to neural networks as just “another tool in your machine learning toolbox”. They have some pros and cons, they work here or there, and sometimes you can use them to win Kaggle competitions. Unfortunately, this interpretation completely misses the forest for the trees. Neural networks are not just another classifier, they represent the beginning of a fundamental shift in how we write software. They are Software 2.0.