What is Deep learning?
Ever wondered how google translates an entire webpage to a different language in a matter of seconds or your phone gallery groups images based on their location all of this is a product of deep learning.
How deep learning works
Deep learning is a type of machine learning in which multi-layered neural networks – imitates to work like the human brain -“learn” from large amounts of data. Within each layer of the neural network, algorithms with deep learning perform calculations and make predictions repeatedly, progressively “learning” and gradually improving the accuracy of the outcome over time. Deep Learning is also used for decision making in fields like autonomous car driving, across apps in computer vision, conversational AI and recommendation systems.
Deep learning vs. machine learning
Simply said, deep learning is a way to automate predictive analytics. While machine learning algorithms are linear, deep learning algorithms are built in a hierarchy of increasing multiplicity and abstraction.
- If we have to create a machine that could differentiate between potatoes and tomatoes. If done using machine learning we’d have to tell the Machine the features based on which the two can be differentiated. These features could be the size and the type of stem on them. With deep learning on the other hand the features are picked out by the neural network without human intervention of course that kind of independence comes at the cost of having a much higher volume of data to train our machine now.
let’s imagine into the working of neural networks. Here we have three students each of them write down the digit 6 on a piece of paper.
Notably they don’t all write it identically. The human brain can easily recognize the digits, but what if a computer had to recognize them. That’s where deep learning comes in.
Here’s a neural network trained to identify handwritten digits.
- Each number is present as an image of 24 times 24 pixels which amounts to a total of 576 pixels.
- Neurons the core entity of a neural network is where the information processing takes place each of the 576 pixels is fed to a neuron in the first layer of our neural network.
- This forms the input layer on the other end we have the output layer with each neuron representing a digit with the hidden layers existing between them.
- The information is transmitted from one layer to another over connecting channels. Each of these has a value attached to it and hence is called a weighted Channel.
- All neurons have a unique number associated with it called bias. This bias is added to the weighted sum of inputs reaching the neuron which is then applied to a function known as the activation function.
The result of the activation function determines if the neuron gets activated. Every activated neuron passes on information to the next layers. This continues up till the second last layer the one neuron activated in the output layer corresponds to the input digit the weights and bias are continuously adjusted to produce a well-trained network.