Both machine learning and deep learning are core technologies of artificial intelligence.
However, there are important differences between them.
- Machine learning is a technique used to help computers learn using modeled training based on results gathered from large datasets.
- Deep learning is a type of machine learning based on artificial neural networks modeled after the original functioning of the human brain. It can be considered as “enhanced machine learning” because it takes the basic functions of machine learning to a higher level.
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Comparing machine learning and deep learning
Machine learning is a very advanced technology. Some of the tasks you can do with it seem like miracles. Deep learning is more complex, but more limited in scope. Setup and analysis typically require more time and resources, but the deeper and better the conclusions. In contrast, machine learning solutions are more narrowly defined, applied to smaller datasets, and can often be reached faster.
Deep learning takes longer
Because deep learning platforms take longer to analyze datasets, they typically take much longer to set up and even longer to reveal results. It usually requires more computational and processing power.
Machine learning can be more specific and faster
Apply machine learning algorithms to specific problems to quickly solve or improve them. For this reason, machine learning has become a very popular use of artificial intelligence in the enterprise.
Deep learning gets deeper
Machine learning can sift through data and identify patterns, while deep learning can analyze larger datasets to detect more subtle anomalous patterns.
Deep learning is “smarter”
Deep learning can learn better from mistakes and adapt to get better results next time.
Both machine learning and deep learning are invaluable tools in helping humans deal with problems and offloading repetitive manual labor. Both play a role in developing more intelligent future applications.
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How does machine learning work?
Machine learning uses computerized systems that can learn and adapt automatically without the need for continuous instruction. Once set up, the system applies itself to a dataset or problem to identify situations or solve problems. Machine learning can draw inferences, address complex problems, and solve them automatically.
draw inferences
Machine learning is based on algorithms and statistical models that analyze patterns found in data and draw inferences from them.
create an automated solution
An algorithm is a procedure designed to automatically solve a well-defined computational or mathematical problem or complete a computer process.
solve complex problems
Algorithms go beyond computer programming because they require an understanding of the different possibilities available when solving a problem.
Machine learning algorithms can therefore be considered a key building block of modern AI. Machine learning finds patterns and anomalies in the noise of data and finds its way to solutions in timeframes not humanly possible. It also helps give autonomy to data models and emulate human cognition and understanding.
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How does deep learning work?
Deep learning systems use multiple layers of processing to incrementally extract better and more sophisticated insights from data. It can be viewed as a more sophisticated application of machine learning that makes heavy use of machine learning algorithms, is inspired by the human mind, keeps learning from its mistakes, and can solve highly complex problems.
Use machine learning algorithms
Deep learning systems use standard machine learning techniques and can be considered a subset of machine learning. But in terms of problem-solving ability, it is almost always more sophisticated than machine learning.
inspired by humans
The mathematical constructs that make up deep learning are loosely inspired by the structure and function of the brain. That means it can handle more nuances and get closer to the human way of thinking about creativity.
Enable continuous learning
Deep learning applications can learn by example and modify their actions based on detected errors, thus continuing to learn and improving their level of accuracy.
including high complexity
Deep learning allows machines to tackle problems of similar complexity that humans can solve.
Deep learning has thus enabled researchers to scale up the models they use in ways that go far beyond traditional machine learning. Deep learning opens new doors to analysis and problem solving by harnessing multiple forms of machine learning systems, models, and algorithms.
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Machine learning use cases
Machine learning has so many use cases. In fact, machine learning permeates nearly every imaginable area where computers are used. For example, it is used in analytics, high speed processing, computation, facial recognition, cyber security, human resources, etc.
drive analytics
Data analytics systems are getting faster and smarter by leveraging machine learning. Today, almost all enterprise data analytics applications incorporate machine learning.
perform the calculation
Just as pocket calculators have nearly replaced manual addition and multiplication, machine learning handles mathematical calculations in nearly infinite proportions.
enable face recognition
Machine learning algorithms can identify identities among millions of candidates as part of facial recognition systems.
Help with cyber security
Machine learning is now part of network monitoring, threat detection and cybersecurity remediation technologies.
Human Resources (HR) Support
Incorporating machine learning into recruiting tools can speed up the hiring process by providing more efficient applicant tracking, employee sentiment analysis, and overall productivity.
Machine learning is therefore used to find needles in haystacks that consist of large amounts of data. It is tied to big data in that these algorithms can be used to scan structured data, unstructured data, and social media feeds.
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Use cases for deep learning
Deep learning use cases go beyond machine learning use cases. Machine learning can be widely applied to a very wide range of tasks. As the name suggests, deep learning is used to solve problems on a deeper and more complex level. Deep learning is used to generate text, automatically deliver meeting transcripts, capture data from documents, and generate video content from text.
Text generation
Deep-learning-based large-scale language models can generate highly reliable and detailed text on various topics, or generate realistic images from text prompts.
Create transcript
Deep learning has been used to provide highly accurate text transcripts from audio recordings of business meetings and phone calls.
Capture data automatically
With the introduction of deep learning, data can be automatically obtained from business documents with high accuracy.
video content production
Another emerging use case is automatically generating video content from text. In this case, the video often uses a virtual avatar for the on-screen speaker.
Deep learning use cases offer multifaceted answers to complex situations and problems. This improves machine learning in terms of scale and depth of analysis.
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Conclusion: Machine Learning and Deep Learning
In many ways, machine learning and deep learning are considered cousins, if not siblings. Each of them consists of algorithms that address complex challenges. However, deep learning uses more sophisticated models that take longer to set up and require more time to process the large data sets you typically analyze.
As such, deep learning is used with a much smaller user base due to the time and cost involved in building and running the system.
However, over time, the required investment will decrease. Perhaps in a year or two, the separation of machine learning and deep learning will be a hot topic. This technology could progress to the point where deep learning techniques become highly accessible and begin to be broadly applied to problems that are currently addressed by the more limited machine learning algorithms.
Related Topics: Algorithms and AI
