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Machine learning and deep learning are both core technologies of artificial intelligence.
Yet there are key differences between them:
- Machine learning is a technique used to help computers learn using training that is modeled on results gleaned from large data sets.
- Deep learning is a form of machine learning based on artificial neural networks that are modeled after the native capabilities of the human brain. It can be viewed as “machine learning on steroids” as it takes the basic capabilities of machine learning to a higher level.
For more information, also see: Best Machine Learning Platforms
How Machine Learning Compares to Deep Learning
Machine learning is highly advanced technology; some of the tasks that can be done with it seem miraculous. Deep learning is still more complex but has a more limited set of applications. It typically requires more time and resources to set up and analyze but provides deeper and better conclusions. In contrast, machine learning solutions can often be arrived at faster as they are more narrowly defined and apply to a smaller data set.
Deep Learning Takes More Time
As deep learning platforms take time to analyze data sets, it typically take far longer to set up and longer to reveal its results. More compute and processing power is usually involved.
Machine Learning Can be More Specific and Faster
Machine learning algorithms can be unleashed on a specific issue to solve or improve it rapidly. Because of this, machine learning has become a very common enterprise use of artificial intelligence.
Deep Learning Goes Deeper
Machine learning can sift through data to spot patterns while deep learning can analyze a much larger data set and detect more subtle patterns of anomalies.
Deep Learning is “Smarter”
Deep learning is better able to learn from mistakes and adapt to do better next time.
Machine learning and deep learning are both incredibly valuable tools in assisting humans in addressing problems and in removing the burden of repetitive manual labor. Both will play a role in the development of a more intelligent future applications.
Also see: Generative AI Companies: Top 12 Leaders
How Does Machine Learning Work?
Machine learning users computerized systems that can learn and adapt automatically without the need for continual instruction. Once set up, the system applies itself to a dataset or problem, spots situations or solves problems. Machine learning can draw inferences, address complex problems and solve them automatically.
Draws Inferences
Machine learning is based on algorithms and statistical models that analyze and draw inferences from patterns discovered within data.
Creates Automatic Solutions
Algorithms are procedures designed to automatically solve well-defined computational or mathematical problems or to complete computer processes.
Solves Complex Problems
Algorithms go beyond computer programming as they require understanding of the various possibilities available when solving a problem.
Machine learning algorithms, then, can be regarded as the essential building blocks of modern AI. Machine learning finds a pattern or anomaly amongst the noise of data and finds paths to solutions within a time frame that humans would not be capable of. They also help impart autonomy to the data model and emulate human cognition and understanding.
Also see: Top Generative AI Apps and Tools
How Does Deep Learning Work?
Deep learning systems use multiple processing layers to extract progressively better and more high-level insights from data. It can be viewed as a more sophisticated application of machine learning that makes heavy use of machine learning algorithms, are inspired by the human mind, can keep learning from their mistakes and 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. Yet it is almost always more sophisticated than machine learning, in terms of its problem solving ability.
Is Human-Inspired
The mathematical structures that comprise deep learning have been loosely inspired by the structure and function of the brain. Meaning it can handle more nuance and come closer to what humans think of as creativity.
Enables Continuous Learning
Since deep learning applications can learn by example and correct their actions based on errors detected, they keep learning and improving their level of accuracy.
Contains High Complexity
Deep learning allows machines to tackle problems of similar complexity to those humans can solve.
Thus, deep learning has enabled researchers to scale up the models they use in a way that goes well beyond traditional machine learning. By utilizing multiple forms of machine learning systems, models and algorithms, deep learning opens new doors for analysis and problem solving.
Also see: What is Artificial Intelligence?
Machine Learning Use Cases
Machine learning has a great many use cases. In fact, machine learning has crept into just about every conceivable area where computers are used. For example, it is used in analytics, rapid processing, calculations, facial recognition, cybersecurity, and human resources.
Drives Analytics
Data analytics systems are made faster and smarter by harnessing machine learning. At this point, most all enterprise data analytics applications incorporate machine learning.
Performs Calculations
Just as pocket calculators largely replaced manual addition and multiplication, machine learning takes care of mathematical calculations of almost infinite proportions.
Enables Facial Recognition
Machine learning algorithms can find an identity among millions of candidates as part of facial recognition systems.
Assists Cybersecurity
Machine learning is now part and parcel of network monitoring, threat detection and cybersecurity remediation technology.
Supports Human Resources (HR)
When incorporated into recruitment tools, machine learning brings about more efficient tracking of applicants, analysis of employee sentiment, overall productivity and can speed the hiring process.
Machine learning, therefore, is employed to find needles in haystacks consisting of massive quantities of data. It ties into big data in that these algorithms can be utilized to scan structured and unstructured data, and social media feeds.
For more information, also see: Top AI Software
Deep Learning Use Cases
Deep learning use cases go beyond those of machine learning. Machine learning is broadly applicable to a huge range of tasks. As the name implies, deep learning is harnessed to solve problems at 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.
Generate Text
Deep learning-based, large language models can generate credible and in-depth text on various topics or generate realistic images from text prompts.
Create Transcripts
Deep learning is being used to provide high-accuracy text transcripts from audio recordings of business meetings and phone calls.
Automatically Capture Data
Deep learning can be deployed to automatically capture data from business documents with high accuracy.
Produce Video Content
Another use case that is emerging is to generate video content from text automatically. In this case, the video often uses virtual avatars for the onscreen speakers.
Deep learning use cases provide multi-faceted answers to complex situations and problems. It elevates machine learning in terms of scale and depth of analysis.
On a related topic: Top Natural Language Processing Companies
Bottom Line: Machine Learning vs. Deep Learning
In many ways, machine learning and deep learning can be viewed as cousins, if not siblings. They each comprise algorithms that are addressed to complex challenges. Deep learning, though, utilizes more sophisticated models that take longer to set up and require more time to crunch through the larger data sets they typically analyze.
As such, deep learning is in use among a much smaller user base due to the time and cost required to build and run its systems.
But as time goes on, the necessary investment is diminishing. Perhaps within a year or two, the separation between machine learning and deep learning will become a moot point. The technology could advance to the point where deep learning techniques become so accessible that they begin to be applied broadly to problems that are currently the province of more limited machine learning algorithms.
On a related subject: Algorithms and AI