Deep Learning - Deep Learning Explained | What is Deep Learning? | Future of Deep Learning

Deep Learning - Deep Learning Explained | What is Deep Learning? | Future of Deep Learning

It is one of the most popular software platforms used for Deep Learning and contains powerful tools to help you build and implement artificial neural networks.

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 

But what exactly is deep learning?

Deep learning is a subset of machine learning which in turn is a subset of artificial intelligence. 

Artificial intelligence is a technique that enables a machine to mimic human behavior. 

Machine learning is a technique to achieve AI through algorithms trained with data and 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. 

Let's understand deep learning better and how it's different from machine learning.

Say we create a machine that could differentiate between tomatoes and cherries 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 dive into the working of Neural Networks

Here we have three students each of them write 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. 

So where is 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 that seem like science fiction is now a reality Tesla, Google, Apple, Tata, and Nissan are only a few of the companies working on self-driving cars. So deep learning has a vast scope but it too faces some limitations.

The first as we discussed earlier is DATA 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.

That brings us to our second limitation COMPUTATIONAL POWER

Training and neural network require graphical processing units which have thousands of courses 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.

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