Deep Learning is a new area of Machine Learning research that has been gaining significant media interest owing to the role it is playing in artificial intelligence applications like image recognition, self-driving cars and most recently the AlphaGo vs. Lee Sedol matches. Recently, Deep Learning techniques have become popular in solving traditional Natural Language Processing problems like Sentiment Analysis.
For those of you that are new to the topic of Deep Learning, we have put together a list of ten common terms and concepts explained in simple English, which will hopefully make them a bit easier to understand. We’ve done the same in the past for Machine Learning and NLP terms, which you might also find interesting.
A perceptron will take several inputs and weigh them up to produce a single output. Each input is weighted according to its importance in the output decision.
Artificial Neural Networks (ANN) are models influenced by biological neural networks such as the central nervous systems of living creatures and most distinctly, the brain.
ANN’s are processing devices, such as algorithms or physical hardware, and are loosely modeled on the cerebral cortex of mammals, albeit on a considerably smaller scale.
Let’s call them a simplified computational model of the human brain.
A neural network learns by training, using an algorithm called backpropagation. To train a neural network it is first given an input which produces an output. The first step is to teach the neural network what the correct, or ideal, output should have been for that input. The ANN can then take this ideal output and begin adapting the weights to yield an enhanced, more precise output (based on how much they contributed to the overall prediction) the next time it receives a similar input.
This process is repeated many many times until the margin of error between the input and the ideal output is considered acceptable.
A convolutional neural network (CNN) can be considered as a neural network that utilizes numerous identical replicas of the same neuron. The benefit of this is that it enables a network to learn a neuron once and use it in numerous places, simplifying the model learning process and thus reducing error. This has made CNNs particularly useful in the area of object recognition and image tagging.
CNNs learn more and more abstract representations of the input with each convolution. In the case of object recognition, a CNN might start with raw pixel data, then learn highly discriminative features such as edges, followed by basic shapes, complex shapes, patterns and textures.
Recurrent Neural Networks (RNN) make use of sequential information.;