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 that is because of deep learning.
What is Deep Learning?
All of this is a product of deep learning but what exactly is deep learning. Deep learning is a subset of machine learningand machine learning is a subset of artificial intelligence. Artificial intelligence is a technique that enables a machine to mimic human behaviour. Machine learning is a technique to achieve AI through algorithms trained with data.
Finally, deep learning is a type of machine learning. Inspired by the structure of the human brain in terms of deep learning. This structure is called an artificial neural network (ANN).
Let us understand deep learning better and how it is different from machine learning.
Say we create a machine that could differentiate between tomatoes and cherries. If done using machine learning we did 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.
Working of ANN
Now let’s dive into the working of neural networks. Here we have three students each of them writes down the digit nine 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 28 times 28 pixels. Now that amounts to a total of 784 pixels neurons. The core entity of a neural network is where the information processing takes place.
Each of the 784 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 trans 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 following 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.
Application of Deep learning
Deep learning applied in customer support when most people converse with customer support agents, the conversation seems so real they don’t even realize that it’s actually a bot.
On the other side in Medical care neural networks detect cancer cells and analyze MRI images to give detailed results.
Self-driving cars what seem like science fiction is now a reality. Apple Tesla and Nissan are only a few of the companies working on self-driving cars.
Limitation of Deep Learning
So deep learning has a vast scope, but it too faces some limitations. While deep learning is the most efficient way to deal with unstructured data. A neural network requires a massive volume of data to train.
Let’s assume we always have access to the necessary amount of data processing. This is not within the capability of every machine and that brings us to our second limitation computational power training and neural network requires graphical processing units which have thousands of course as compared to CPUs and GPUs. Are of course more expensive?
Finally, we come down to training time deep neural networks take hours or even months to train. The time increases with the amount of data and number of layers in the network.
Deep Learning Frameworks
Some of the popular deep learning frameworks include TensorFlow, Pytorch, Keras, DL4J, Caffe and Microsoft cognitive toolkit considering the future predictions for deep learning and AI.
Technology is working on a device for the blind that uses deep learning with computer vision to describe the world to the users. Replicating the human mind at the entirety may be not just an episode of science fiction or too long. The future is indeed full of surprises and that is deep learning for you in short.
