Deep learning (DL) and Machine Learning (ML) have dominated the world of Artificial Intelligence (AI). The first DL and ML revolution started in 2012 and the tech world has never looked back since. Continuous advancements have also marked the journey of DL/ML-based AI.
Both these advancements in technology have evolved a lot over the past few years. They both provide systems with the ability to experience and learn without any human input.
In this article, we will highlight the key features of DL and ML-based technologies and how they differ from each other in terms of advanced features and their applications based on different parameters.
What is Machine Learning (ML)?
ML refers to the computer systems that can automatically learn and adapt using historical data without following any explicit instructions. ML is a subset of Artificial Intelligence. It uses statistical tools and algorithms to build mathematical prediction models. Whenever new data is received, it analyzes and deduces inferences, and predicts output based on the patterns in data. However, the prediction accuracy is directly proportional to the amount of data processed. So, huge data sets help build better and more accurate data prediction models.
ML algorithms build a model based on training data and use it to make predictions. However, not all ML is statistical. Mathematical optimization research also aids ML by providing tools, theory, and application sectors.
What is Deep Learning (DL)?
DL is another subset of AI that operates based on Artificial Neural Networks. It uses multiple layers of processing to extract valuable insights from the data. It is a specialized system in the family of ML methods. It also operates on large amounts of data to make approximate predictions. Additional layers in DL allow users to refine and optimize data structure analysis.
DL is also known as deep structured learning and is categorized into three types including, unsupervised, semi-supervised, and supervised learning. Deep-learning architectures include deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks, and convolutional neural networks. These are used to improve process automation and data analytics without human intervention. It forms the basis of many products that we use in our everyday lives. Some of the examples include digital assistants, self-driving cars, voice-enabled TV remotes, etc.
5 Major Differences between Deep Learning and Machine Learning
Both DL and ML facilitate the processing of unstructured data, such as documents, photos, and text. However, they exhibit some key differences as follows:
- Solution approach: The traditional ML model solves a problem in sub-parts and produces the result in the same manner. However, DL follows an end-to-end approach. It takes the input off a given problem as a whole and directly processes the final result.
- Automation level: DL can operate as a standalone system, unlike ML which sometimes requires human intervention and supervision. DL can also learn from errors and improve in the subsequent operations which ML cannot. It is possible because DL is backed by the hierarchy structure of neural networks.
- Hardware requirement: ML systems can operate on low-end machines like regular computers as it does not process much amount of data. On the other hand, DL requires high-end machines with GPU support as it relies on huge data sets for providing efficient output. The growing use of photographic processing devices has resulted from the elevated call for power and due to string parallelism, GPUs are useful for excessive bandwidth reminiscence. They offer cap potential to hide latency (delays) in reminiscence transfer.
- Time efficiency: ML systems are simple to set up and operate, but the results they provide are often constrained and take a longer time to process. Whereas DL takes longer to install but produces instant results. Hence, takes lesser time to process.
- Applicability: Inboxes, banks, and doctor’s offices are all using ML. DL technology enables more complicated and autonomous programs, such as self-driving automobiles and surgical robots.
Other differences between DL and ML include:
| Parameters | Machine Learning | Deep Learning |
| Data interpretation | Easy | The final output is sometimes difficult to understand. |
| Supported data type | Structured data | Both structured and unstructured data. |
| Specialized feature | Best-suited for solving simple problems. | Best-suited for complex problems. |
| Output format | Numerical value or score. | Numerical value, text, sound, or any other free-form element. |
| Data analysis | Examines specific variables in datasets. | Largely performs self-depicted data analysis. |
| Functionality | Predicts future actions from input data sets. | Interprets data features and relations. |
Future trends of DL and ML
ML and DL will extend their applications in the coming years. Apart from various industry verticals, these technologies are also expected to play an important role in the fields of academics and research. Artificial neural networks that form the base of DL have already revolutionized the existing technologies. ML-based operations have also witnessed a significant reduction in error rates and improved performance efficiency of networks.
Tech enthusiasts have predicted further advancements in DL and ML models. Some of the popular predicted trends of 2022 include the introduction of independent and full-stack DL, a dive into a convolutional neural network, multi-modal learning, integrated hybrid models, automated data labeling, etc.
Conclusion
ML algorithms have applications in medicine, email filtering, computer vision, speech recognition, etc., especially when it is not feasible to develop conventional algorithms for the completion of needed tasks.
DL, on the other hand, has applications in natural language processing, machine translation, automated speech, bioinformatics, drug design, medical image analysis, medical research, climate science, material inspection, defense system, board game programs, and cyber security producing results that are comparable to, and in some cases superior to, traditional approaches.
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