Introduction

 

The foundational technologies for the upcoming computing revolution are artificial intelligence and machine learning. These technologies depend on the ability to recognize patterns and then predict future outcomes based on data observed in the past. This explains Amazon's suggestions when you shop online or how Netflix knows your penchant for foul 80's movies. Although machines using artificial intelligence principles are often described as "smart", most of these systems do not learn by themselves; the intervention of human programming is necessary. Data scientists prepare inputs and select variables for predictive analysis. Deep learning, however, is capable of carrying out this task automatically.

 

 

What is Deep Learning?

 

Deep learning can be considered a subset of machine learning. It is a field based on learning and improving oneself by exploring computer algorithms. Deep Learning uses artificial neural networks that are made to resemble human neural networks, in opposition to machine learning, which utilizes simpler principles. Up until recently, the complexity of neural networks was constrained by computational capacity. But improvements in big data analytics have made it possible for more elaborate neural networks, which let computers monitor, pick up on, and react to complex events more quickly than people. Speech recognition, language translation, and image categorization have all benefited from deep learning. It can be used to automatically solve any pattern recognition issue.

 

Artificial neural networks, comprising many layers, support deep learning. Deep neural networks (DNNs) are networks where each layer can perform complex operations such as representation and abstraction that make sense of images, audio, and text. Considered to be the fastest-growing field of machine learning, deep learning is a truly disruptive digital technology. More and more enterprises are utilizing it to create new business models.

Now that you understand what Deep Learning is let's start knowing how Deep Learning works.

 

 

Our Learners Also Read: Which Machine Learning Method is Unsupervised Learning?

 

 

How Does Deep Learning Work?

 

Through a mix of data inputs, weights, and biases, a deep learning neural network or artificial neural network tries to replicate the functioning of the human brain. Together, these components accurately identify, categorize, and characterize the objects in the data.

Deep neural networks are constructed of many layers of interconnected nodes, each of which improves on the prediction or categorization made by the one underneath it. Forward propagation is the term used to describe this kind of network calculation. A deep neural network's visible layers are its input and output layers. The deep learning model gets the data for processing in the input layer, and the final prediction or classification is completed in the output layer.

 

 

A different method known as backpropagation calculates prediction errors using methods like gradient descent before modifying the weights and biases of the function by going back through the layers to train the model. The neural network is able to predict and rectify any faults as necessary thanks to forward and backward propagation working together. The algorithm is gradually improved over time.

In the simplest terms possible, the aforementioned summarizes the simplest kind of deep neural network. However, there are other varieties of neural networks and deep learning algorithms that are highly sophisticated.

 

Mainly used in computer vision and image classification applications, convolutional neural networks (CNNs) can detect features and patterns in an image, enabling object detection or recognition tasks. In 2015, CNN beat a human for the first time in an object recognition challenge.

Recurrent Neural Networks (RNNs) are typically used in natural language and speech recognition applications because they use sequential or time series data.

 

 

How Does Deep Learning Achieve Such Impressive Results?

 

Simply put, accuracy. Deep learning achieves higher degrees of recognition accuracy than ever before. This is crucial for safety-critical applications like driverless cars and helps consumer electronics live up to customer expectations. Deep learning now performs better than humans in several tasks, like classifying objects in photos, according to recent improvements.

Although deep learning was first postulated in the 1980s, it has only lately started to be helpful for a number of purposes for two key reasons:

  • Numerous labeled data sets are necessary for deep learning. For instance, creating a driverless car involves countless photo and video hours and millions of photos.
  • Deep learning calls for a lot of processing power. Deep learning is effectively supported by the parallel design of high-performance GPUs.

 

 

Real-Life Examples of Deep Learning

 

Deep learning is generating a lot of conversation about the future of machine learning. Technology is evolving rapidly, generating both fear and excitement. While most people understand machine learning and artificial intelligence, deep learning is the “new kid on the block” in tech circles and is causing anxiety and excitement.

Deep learning is also known as neurally organized learning and occurs when artificial neural networks learn from large amounts of data. Deep Learning algorithms execute tasks repeatedly, pinching them each time to improve the result. Algorithms rely on extensive amounts of data to support "learning".


Some of the jobs that deep learning can support are listed below:

 

Virtual Assistants

 

Use Siri, Cortana, or Alexa? When communicating with users, popular virtual assistants use deep learning to comprehend their language and vocabulary. They improve their ability to provide the necessary information as a result.

 

Driverless Vehicles

 

Autonomous vehicles are already on our roads. Deep learning algorithms help determine whether an object on the road is a paper bag, another vehicle, or a child and react accordingly.

 

Chatbots

 

Chatbots are becoming more common and can be found on numerous frequently visited websites. Chatbots using deep learning can respond more intelligently to an ever-increasing number of questions. The deeper the data set from which deep learning takes place, the faster deep learning can produce the desired results.

 

Face Recognition

 

From tagging people on social media to vital security measures, facial recognition is essential. Deep learning makes it possible for algorithms to function correctly in spite of aesthetic changes like hairstyles, beards, or poor lighting.

 

Medical Science

 

There are around three billion base pairs of DNA chromosomes in the human genome. In addition to being studied and used for various pharmaceutical and medical objectives, machine learning is assisting researchers and medical professionals in the creation of tailored medicines and tumor diagnoses.

 

 

Benefits of Deep Learning:-

 

  • Unlike machine learning, deep learning models perform feature extraction and classification at the same time. So instead of building two different models, the two tasks can be done in parallel by building on one model.
  • Deep Learning models can literally process data in sheer volume at high GPU processing speed.
  • Deep network models can learn highly complex features using a backpropagation algorithm.
  • There are several open-source libraries like Keras, Pytorch, and TensorFlow that we can use to quickly build a deep learning model.

 

 

Summary

 

Deep learning has improved data processing models and can generate accurate results when processing complex Data Science tasks. As we clearly realize, Machine Learning is suitable for processing labeled data; in contrast, Deep Learning promotes unsupervised learning techniques and allows systems to become more sophisticated and brilliant without much human intervention.