Deep learning, it is a subset of machine learning which is further a subset of Artificial Intelligence. It focuses on creating a large artificial neural network that are capable of making accurate data-driven decisions.
Before getting into the details we must have a basic understanding of machine learning.
What is Machine Learning?
In machine learning we provide computer a model referring which it can evaluates the examples. It is far different approach from providing a computer with massive list of rules that it should use to solve complex problems.
Along with the model we also provide computer a small set of instructions that it can use to modify the model when it makes mistakes.
The most known examples of machine learning are:
- Speech and image recognition
- Traffic alerts in Google map
- Google Translations
- Chatbots & many more…
This technology has the ability to recognize the patterns in the data observed and based on this observation they can predict the future outcome. Like based on your online purchase Amazon offers you a deal, based on recently watched movies, Netflix proposes your similar kind of movies.
Although these systems are referred as ‘smart’ but most of them do not learn or themselves on their own. Some human intervention is required in the form of programming. On the other hand, deep learning can do this job automatically.
Deep Learning in Artificial Intelligence
- What is Deep Learning?
- How Deep Learning Works?
- Deep Learning Methods
- Applications of Deep Learning
- Future Scope
What is Deep Learning?
Deep learning emerged as a research area in the field of machine learning where the basic idea is learning from examples. Deep learning creates a deep-hierarchical levelled learning architecture that imitates the biological neuron network of human brain.
The actual biological neural network can make approximate predictions with the single layer of neurons. However, with the additional hidden layers of neurons, the network can optimize and refine the data for accuracy.
Now, having many hidden layers in artificial neural network is deep learning. But remember, more is number of layers means longer training and slower usage. We refer the artificial neural network with many layers as Deep Neural Network (DNN) where each layer is capable of performing complex operations.
How Deep Learning Works?
The Deep Neural Network imitates neural network of human brain. Like, biological neural network is made up of millions of interconnected neurons, the artificial neural network is made up of interconnected artificial neuron.
These neurons are nothing but the software modules. These modules use mathematical calculations to process the input data. Like biological neural network, the deep neural network has multiple layers of interconnected nodes where each layer is built on the previous layer which helps in refining and optimizing the prediction or classification. We refer this process of forwarding the processed data to the next layer as forward propagation.
The input layer from where the deep learning model ingest the data for processing and the output layer where the final prediction or classification is made are the only visible layer of any deep learning model.
A deep learning model also uses a backpropagation mechanism to calculate errors in the prediction. The forward and backpropagation mechanisms together train the deep learning model to make the accurate prediction and also to correct the occurred errors accordingly. Over the time the trained model gradually provides the accurate prediction.
This is the working of simplest deep learning algorithm. Although there are some complex deep learning algorithms such as:
- Convolutional Neural Networks – Commonly used in computer vision that enables a computer to interpret and classify the image or visual data.
- Recurrent Neural Networks – Commonly used to process sequential data or time series data.
Deep Learning Methods
The various methods to create deep learning models are:
1. Learning Rate Decay – It is factor defining the system prior to the learning process. This factor controls how much change the model will experience in response to the error each time it occurs. Too much high learning rate leads to unstable training processes. However, too much smaller learning rate leads to lengthy training processes.
2. Transfer Learning – It is a method used to perfect a previously trained model. Transfer learning method requires much less data to train more the already trained model.
3. Training from Scratch – It is a method used to train the new developed model. Thus, the developer requires a large labelled data set. Training may require days or weeks.
4. Dropout – With this method the model solves the problem of overfitting in network. The method dropout the random amount of parameters from the network and also their connection from the network during training.
Applications of Deep Learning in Artificial Intelligence
In real world, the deep learning models are so well integrated with the products. So much so that even the users are unaware of the complex data processing taking in the background of that product or services.
Deep learning algorithm are capable of identifying the dangerous pattern from any transactional data which enable it reveal fraudulent or criminal activities. Features of deep learning such as speech recognition, computer vision, etc. improves the efficiency and effectiveness of any investigation by extracting the patterns and evidences from audio recording, cctv footage, images and documents. This all helps the law enforcement to analyze large amount of data quickly and accurately.
Deep learning algorithm drive predictive analysis on stock trading, business risk for loan approval to detect any kind of fraud and even help in managing investment and credit portfolios of client
The most common deep learning application of customer service are the Chatbots that widely used in various application. The most common example of it are Apple’s Siri, Amazon Alexa, or Google Assistant.
The deep learning applications are greatly benefiting the health care industry. Since there has been digitization of record and images the experts are able to analyze and assess more records and images in less time.
Integrating deep learning algorithms with cloud infrastructure can help in overcoming the challenges like large quantity of available data and large processing power. Integrating deep learning with the cloud will help in designing, developing and training the deep learning models more faster.