Deep Learning

While machine learning algorithms worked well, there was still a significant error rate in image recognition and the accuracy of machine learning algorithms that were used to classify images was not satisfactory. 

At this point, the researchers, inspired by the way the human brain functions, modelled a computing unit – the Artificial Neuron!

Figure 8 : The Neural Network inspired by Human Brain

 

The new architectures which were then developed, could help in building models that could represent complex relationships between variables. These were the Artificial Neural Networks. 

The result was a simplified model that had 3 layers – Input Layer, Hidden Layer and Output Layer. As the number of hidden layers are increased, very complex models can be built. The word ‘Deep’ in Deep Learning refers to the large number of hidden layers in networks. 

With the Artificial Neural Networks, the accuracy of image classification improved, so much so that the latest deep network architectures have achieved over 90% accuracy on very large image datasets. 

With suitable modifications, the Neural Network can be used for different tasks. The given diagram illustrates the same.