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There is a lot of buzz around artificial intelligence and its different algorithms. We all are quite aware that machines along with specific computer algorithms can do wonders in our homes, offices or at workplaces. With the advancement of technology, one must know the main reasons behind several hi-tech inventions and innovations, is the new concept of deep learning.

Ever wondered how Netflix or Amazon showing recommendations based on your preferences, or how Siri is able to respond to each and every request. It means, deep learning algorithms are at work.

Right now, it may leave you a little perplexed, but heres a simple guide on deep learning, how it works and how it is deeply associated with the AI world. Forbes Advisor India has broken down this particular branch of AI in most simple terms.

Deep learning runs many artificial intelligence (AI) applications and services. It helps in adding intelligence and improving automation to the existing AI enabled products. DL is that part of AI which helps in performing analytical and physical tasks without any sort of human intervention.

In short, deep learning is a complex technique of machine learning, which instructs computers to learn or respond as to what naturally comes to humans. So, whether it is driverless cars, hands-free speakers, voice recognition in phones, tablets, TV or watches, deep learning is a major force behind all these breakthrough innovations.

In the concept of deep learning, the computer learns to perform on the basis of direct data feed such as image, text or sound. Such models are capable of achieving super accurate results and sometimes much better and more efficiently than human beings. Models based on deep learning uses a large set of data which requires high computation power and responds accurately via using a neural network which contains multiple layers like that of the humans brain.

In nutshell, deep learning sits inside of machine learning, which sits inside of artificial intelligence.

Artificial Intelligence: The development of a computer system which is able to perform all the given tasks at par with human intelligence.

Machine Learning: It is a subset of AI which contains statistical algorithms which enable machines to improve the tasks with experience. As opposed to deep learning, machine learning models need human intervention to improve accuracy.

Deep Learning: It is a kind of machine learning which has a human brain-like structure. It works on the basis of logical assembly of algorithms which are known as neural networks.

In the world of efficiency and accuracy, deep learning has made a notable and dominant position than ever before. Deep learning applications are used in various industries from healthcare, automated driving, medical devices, aerospace and defense, electronics and industrial automation.

Deep learning is extensively used in automated hearing, speech recognition, language translation, digital assistance, etc.

To give such accurate results, DL requires a large amount of labeled data and high computation power. High-performance GPUs have a perfect and ideal architecture which has been proved efficient for deep learning to perform. When combined with clusters or cloud computing, this technology enables teams to reduce training time for a deep learning network from weeks to hours or may be lesser than this.

For example, driverless car development requires billions of images and hours and hours of video, which help deep learning to automatically detect objects such as pedestrians, stop signs and traffic lights.

Mostly, all the deep learning-based models use architectures related to neural networks. The term deep is associated with this technique as it refers to the multiple number of hidden layers which are present in a neural network.

Deep learning models have been given proper training by using a large subset of labeled data and neural network architectures, which in turn helps these models to learn directly from that data without the need of any human intervention.

Neural networks are generally organized in multiple layers consisting of a different set of interconnected nodes. It is to be noted that these networks have the ability to have tens or hundreds of hidden layers.

Most deep learning features use the transfer learning approach, a procedure which involves fine-tuning a pretrained model. However, the relevant features are not pre trained as they are learned while the whole network trains on a collection of images. This feature includes automated extraction which makes deep learning models very accurate.

The key advantage of deep learning models is that they continue to improve as the size of the data upsurges. The more practice deep-learning algorithms get, the better they become.

Deep learning algorithms are very complex in their nature. There are different types of neural networks which helps in addressing the specific problems or datasets, such as:

Convolutional Neural Network (CNN): This kind of neural network has a certain degree of complexity which is seen in human brains. Neural network is not just made up of one layer but it consists of various layers which are also known as additional convolutional layers or pooling layers. It is to be noted that each layer plays a very crucial role in grasping the data and thus reaching a final conclusion which means identifying bigger portions of the image.

The first layer focuses on simple and basic features, such as colors and as the data progresses the layers of the network start to recognize much bigger elements of the object until it finally identifies the final object.

Recurrent Neural Network (RNN): Recurrent neural networks use consecutive data or time series data to resolve common problems seen in language translation and speech recognition which are used in various applications such as Siri, voice search, and google translate.

Similar to convolutional neural networks (CNNs), the RNN uses training data to learn. They distinguished the fed data by their memory as they take cues from prior inputs, which in turn influences the current input and output.

As we have already learnt, deep learning requires a massive amount of computation power. For this, high performance graphical processing units or (GPUs) are considered as ideal as they are able to handle and process quite a large volume of calculations and patterns with abundant memory available. It is to be noted that managing multiple such GPUs need high requirement of of internal resources which can be incredibly costly

Deep learning plays an important role in AI predictive modeling. It collects massive amounts of data and analyzes it to create multiple predictive models by understanding various patterns and trends within the data. Deep learning models make it very fast and easy to construct large amounts of data and form them into meaningful information. It is widely used in multiple industries, including automatic driving and medical devices. It is just a matter of time as this technology continues to mature.

Read more:
What Is Deep Learning AI & How Does It Work Forbes Advisor INDIA - Forbes