Technological advancement has led to the popularity of deep learning over the last few years. Hundreds of devices generate a lot of data, and this needs to be analyzed or processed using different techniques and tools – hence the need for deep learning.
While deep learning has been termed efficient in terms of accuracy, its application to real-world problems presents some challenges.
Our guide explores some of the strengths of implementing deep learning in real-life scenarios and challenges that must be overcome to ensure accuracy and efficiency.
What is deep learning?
Deep learning is a subset of machine learning that teaches computers to process data similarly to humans. The technology mimics the human brain’s learning process using artificial neural networks.
Deep learning models analyze data from multiple sources in real-time without human intervention. Technologies such as self-driving cars, facial recognition, medical image analysis and speech recognition all use deep learning.
Advantages of implementing deep learning over machine learning
Deep learning has several strengths and benefits over machine learning. These include the following.
Capability to execute feature engineering
In real life, the features in your data influence the results you’ll get with a predictive model. Better features translate into better results. A good example of feature engineering is a competition in which competitors were challenged to predict which passengers survived the Titanic. The company provides a testing and training data set required to train the predictive model.
Deep learning reveals new insights by analyzing patterns
Applying deep learning to large amounts of labeled data can discover patterns and reveal new insights.
For example, when you apply deep learning to consumer purchases, you can determine the type of products that your customers will likely spend on based on their buying patterns.
Deep learning models promote faster and better predictions, which translates to fewer costs and higher revenues for companies.
Deep learning doesn’t require supervised learning
Traditional machine learning requires a considerable variation of labeled datasets. This means that a human needs to label various datasets manually, make the algorithm process the dataset, test the algorithm on a specific dataset, and determine why there are inaccuracies in some results.
Machine learning also requires you to improve the dataset by labeling new datasets to improve result accuracy – hence supervised learning.
Deep learning only relies on human behavior to learn and improve. Unlike machine learning, you won’t need a considerable variation of labeled datasets.
Deep learning can be integrated with existing technologies
Deep learning technology has evolved thanks to its accuracy and efficiency. This has also allowed it to be integrated with existing technologies such as the Internet of Things (IoT) and big data.
Integrating deep learning into the IoT has led to the creation of applications that can handle complex recognition tasks. Data scientists are now creating deep neural networks that can collect data from various IoT devices and work with fewer data labels.
Examples of applications of the IoT with deep learning include smart devices that learn a patient’s symptoms and make predictions, and smart home devices that can clean the house, adjust lights and more.
Deep learning is scalable
Unlike machine learning, which is limited in the data it can process, deep learning is more scalable thanks to its ability to analyze big data effectively. Its scalability permits concurrent computation using graphic processing units.
Deep learning allows unstructured data processing
A considerable portion of a company’s data is unstructured. That’s because most of it is in the form of text, images, symbols and more. Machine learning algorithms can’t interpret this data type as they require manual extraction through human involvement.
Machine learning requires data labeling for the program to analyze data. The process can be time-consuming and expensive.
Deep learning algorithms can analyze unstructured or unlabeled data and uncover insights that are helpful to an organization. Text files such as PDFs/Word documents, entertainment data and weather data cannot be analyzed using machine learning, but deep learning can analyze this data and provide insights.
Challenges of using deep learning in real life
Although data learning’s efficiency and accuracy are beneficial, there are other areas where deep learning models fail short.
Deep learning can be expensive when using complex data models
Deep learning only uses large amounts of data. It also needs extensive hardware such as advanced GPUs with enough storage to perform complex mathematical computations.
Training deep neural networks from scratch can take several weeks to months. The data size will also determine how much computational power you’ll need. The more data involved, the more computational power required, which can be expensive.
Small companies trying to gain insight may find the deployment of deep learning costly in terms of power used and hardware required.
Various factors can affect the accuracy of deep learning
Although deep learning has provided some accurate results in the past, sometimes it’s not reliable. Presenting deep learning with data that’s not in a classified format can lead to erroneous output as its algorithm only works with problems presented in a classification format.
Additionally, it’s challenging to explain how deep learning works. Deep learning cannot explain how it reached a specific conclusion such as machine learning. In case of any errors in the output, you’ll need to modify the whole algorithm. That’s why deep learning cannot be used in specific industries such as financial institutions.
Deep learning requires a lot of data to get the desired results
Unlike machine learning, which can work with any data, deep learning needs extensive data sets to deliver results. The deep learning algorithm requires copious amounts of data for the training process.
Acquiring this vast amount of data is time-consuming and requires advanced processing units capable of processing this data.
Final thoughts
Data learning’s popularity is expected to increase due to its ability to work with unstructured data, efficiency in providing insights, and integration with other technologies. Despite the few challenges that the technology may face, deep learning is expected to impact future technologies. More industries will continue adopting this critical technology to stay ahead of the competition and remain relevant.
can this be reworked? the program is a masters of computer science with a data science concentration – it’s part of the larger program
