Deep Learning

What is Deep Learning in simple words- with Examples

Getting your Trinity Audio player ready...

Machine Learning and Artificial Intelligence have played the role of revolutionaries in the world of computing. They have the ability to do things which currently businesses have been employing humans to perform. Machine Learning is able to based on the data from the past predict patterns and trends of the future. With the speedy evolution of technologies, Machine Learning, Artificial Intelligence and Deep learning meaning might baffle you. This blog would act as a guide for you to understand the concept- What is Deep Learning?- and how it works in the Data Science field. Explaining through examples of Deep Learning, you might find yourself searching for career prospects in the domain as well. 

What is Deep Learning in AI? 

With technological advancement, Machine Learning and Artificial Intelligence have evolved rapidly. A subset of Machine Learning makes use of artificial neural networks and computer algorithms to imitate human learning. In order to improve the outcome of every task, Deep Learning uses machine learning algorithms to perform tasks repeatedly. In order to solve any business problem, critical thinking is required. Deep Learning performs tasks to solve business problems by using neural networks that learn from various levels. It dives deep into the issues and tries to make improvements in business operations. 

Deep learning requires tons of data from where it learns and this enables the creation of insightful data possible. This is one of the primary reasons that Deep Learning has grown and evolved over the years. 

How Deep Learning works? 

Deep Learning uses neural networks which has multiple layers or nodes. Each node within individual layers is connected with adjacent layers. The more the number of layers, the deeper is the network created. Within an artificial neural network, the signals tend to travel between the layers of nodes and assign corresponding weights. If a layer is weighted heavier, the effect on the next layer of nodes will be higher. The final layer before producing the output compiles the collected weights of the nodes of inputs and declares the result. Deep Learning involves complex data processing and mathematical calculations. Therefore, the system hardware requires to be very powerful. However, even if a hardware system is very powerful, training it for neural networks takes weeks. 

How Deep Learning works

Types of Deep Learning 

Let’s now look at some of the different types of Deep Learning: 

Feedforward Neural Network 

  • This is one of the most basic neural networks in Deep Learning. 
  • The network flow control is a process where it travels from the input layer to the output layer. 
  • These kinds of neural networks have one or single hidden layers. However, since data movement is only one way, data cannot propagate backwards. 
  • Some of the weights present within the input layers are fed to the input layer only. 
  • Facial recognition makes use of this algorithm of networks using computer vision 

 Radial Basis Function Neural Networks 

  • This type of Neural Network has more than 1, primarily 2 layers. 
  • The calculation of the relative distance of the network from one point to the centre and continues for different layers. 
  • The use of Radial Basis Function networks is mainly for power restoration systems for the restoration of power in a short span of time. 

Multi-layer Perceptron 

  • Within this type of network, there are more than 3 layers to classify data which is not linear. 
  • Having more than three layers, the networks connect effectively with every node. 
  • The use of these networks is for speech recognition and other ML Technologies. 

Convolution Neural Network (CNN) 

  • This is one of the variations of the multilayer perceptron 
  • It contains more than one convolution layer and it contains a layer which is deeper with fewer parameters. 
  • The use of the CNN network is effective in image recognition and identifying image patterns. 

 Recurrent Neural Network 

  • The type of neural network where the output feeds on the same node as that of the input. 
  • The RNN is the method which helps the network in predicting the outcomes 
  • The use of this network is for the development of chatbots maintaining a small state of memory 
  • Chatbots and text-to-speech use this kind of technology. 

Example of Deep Learning 

The following are real-life examples of Deep Learning that you might find interesting and would make you curious in learning more about it.

  • Virtual Assistants: 

The use of Alexa, Siri, and Cortana act as virtual assistants that make use of deep learning in terms of speech recognition connected with human language. When humans interact through these visual assistants, it is able to interact with them. 

  • Translations: 

The use of Deep Learning is made to automatically translate between different languages. Travellers, business people and government officials make use of translation tools to understand different languages 

  • Chatbots and service bots: 

Use of the Recurrent Neural Network as a type of Deep Learning is used to provide customers with services where it responds intelligently to customer queries. It is also a helpful way of increasing auditory and text questions. 

  • Transforming Image Colours: 

Previously, humans use to make changes and transform black-and-white images to colour format, manually. With the help of algorithms of Deep Learning, it is now possible to make use of the context of the image to recreate the black-and-white image in colour. 

  • Facial Recognition:

Facial recognition is used increasingly as a form of security requirement in social media and even used as password protection. The use of Deep Learning has increasingly facilitated the use of facial recognition for the maintenance of security. However, the challenge with this technology is that when a person makes changes in hairstyle or shaves his beard, it becomes an obstruction to recognising the face. 

Deep Learning Career Prospects 

As the use of Artificial Intelligence spreads in different fields for business purposes, companies lack the expertise and professionals skilled in Deep Learning. However, the demand for Machine Learning Engineers is high because they precisely have the skills for Machine Learning. Further, Glassdoor recognises the average salary of Machine Learning Engineers being %115,000 per annum. On the other hand, PayScale determines the salary range between $100,000 to %166,000. As Deep learning systems continue to evolve and improve in the market, the growth of Deep Learning career prospects would further increase as well. 

Parting Thoughts 

Thus, from this blog, we get an understanding of Deep Learning and its uses for different purposes in different industries in detail. Deep Learning in the Data Science field is continuously evolving and revolutionising the way in which human expertise has been able to transform through Machine Learning systems. If you want to become an expert in this field, you need to take up a Data Science course online by Pickl.AI which offers Machine Learning and Artificial Intelligence as part of its curriculum. You would be able to learn What is Deep Learning and develop skills in Artificial Intelligence.

Author

  • Asmita Kar

    Written by:

    I am a Senior Content Writer working with Pickl.AI. I am a passionate writer, an ardent learner and a dedicated individual. With around 3years of experience in writing, I have developed the knack of using words with a creative flow. Writing motivates me to conduct research and inspires me to intertwine words that are able to lure my audience in reading my work. My biggest motivation in life is my mother who constantly pushes me to do better in life. Apart from writing, Indian Mythology is my area of passion about which I am constantly on the path of learning more.