Context

• The concept behind an Artificial Neural Networks is to define inputs and outputs, feed pieces of inputs to computer programs that function like neurons and make inferences or calculations, then forward those results to another layer of computer programs and so on, until a result is obtained.

About Artificial Neural Networks

• As part of this neural network, a feedback or difference between intended output and the input is computed at each layer and this difference is used to tune the parameters to each program.

  

• This method is called backpropagation and it is an essential component to the Neural Network.
• Instead of CPUs, Graphic Processing Units (GPU) which are good at performing massive parallel tasks can be used for setting up ANNs.
• A few free ANN frameworks are TensorFlow, Keras, PyTorch and Theano.
• These can be used for both normal Machine Learning tasks like classification or clustering and for Deep Learning/ANN tasks.
• Are there tasks that cannot be done with good accuracy by normal Machine Learning and hence need Deep Learning?
  • The answer is yes.
  • Automatic Image Recognition of rich images (instead of only simple hand-written digits) and Speech Recognition are two popular uses of Deep Learning. Convolution Neural Network (CNN), a special type of ANN, is good at Image Recognition.
  • It connects a neuron in a layer to all neurons in the next layer but uses optimisation techniques to weed out unwanted signals from neurons.
  • For Speech Recognition, Recurrent Neural Network (RNN) is used because it is good at handling inputs of variable length like speech.
• ANNs are present in many smartphone applications that we use, like voice to type, Siri and Alexa.

Why called Neural Network?

• Neuron is the building block of the brain and it inspired computer scientists from the 1950s to make a computer perform tasks like a brain does.
• It is not a simple problem and the clue to its complexity is in the brain structure.

The popularity of ANNs

• A few more technical phrases will clear up our understanding of this space. Data Science, used interchangeably with Machine Learning, is the computer technology that uses data to detect patterns.
• Hand-written digit recognition is a good example of machine learning. However, in order for the computer to do this task, large amounts of sample data need to be manually labelled as examples of images of digits.

Source: TH

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