Studying and following up on technological advancements in artificial intelligence (AI) is quite challenging. However, when learning its basics, we come to two major methods: machine learning and deep learning.
These systems have impacted our lives in many ways. For instance, they are the driving force that enables popular platforms like Netflix to pick a suitable show for you and they are also behind the actualization of automobile self-drive technology.
So, are machine learning and deep learning transposable tech words? In this article, we will reveal everything you need to know about these dominant AI concepts, including their differences and similarities. Read on to learn more.
Machine Learning vs. Deep Learning: Side-by-Side Comparison
|Machine Learning||Deep Learning|
|Data||Functions optimally with a small or medium database||Requires a big database|
|Hardware Requirements||It can function with low-end machines||Needs high-end GPU machines|
|Engineering Peculiarity||Understands features and ways they represent data||Needs to understand data’s basic functionality|
|Number of Algorithms||Many||Few|
|Data Interpretation||It is easy to interpret some machine learning algorithms, but others are pretty difficult||Very complex|
|How It Works||Uses several distinct automated algorithms to predict future actions and model functions from data||It uses neural networks that go through various processing layers in order to interpret relations and data features|
|How It’s Managed||Analysts detect algorithms to examine variables in the data sets||Algorithms are self-directed to data analysis more often after being put into production|
|Outputs||Numerical values, like scores or classifications||Anything from free-form elements like sounds and texts to numerical values|
|Optimal Data Volumes||Thousands of data points||Millions of data points|
Machine Learning vs. Deep Learning: What’s the Difference?
Let’s look at the dissimilarities between the two.
Definition and Meaning
Machine Learning is a type of AI that enables the system to learn from its experiences in order to upgrade its operations without human intervention. This AI concept is used in various sectors, including healthcare, art, finance, and science.
On the other hand, deep learning is an upgraded version of machine learning. It uses neural networks, which are designed to mimic the structure and function of the human brain. Furthermore, deep learning systems don’t need human input to function. They can increase their proficiency and accuracy independently using the data provided by the user.
Machine learning systems need a programmer to identify and hand-code applied features depending on the data type (orientation, shape, pixel value). On the other hand, deep learning systems can learn these features without human input. They work similarly to the human brain, and you don’t have to re-code the program once it has been created.
The best example of a deep learning system is the facial recognition system. This system will discover ways of independently detecting and assessing facial edges, lines, and overall face representations. The data involved in conducting this work is massive, so the program will continually train itself as time passes. This training will further boost its accuracy in identifying faces.
Deep learning systems process enormous amounts of data and continuously solve complex mathematical calculations using their algorithms. Therefore, they need powerful hardware to accomplish this task effectively and on time.
High-end graphical processing units (GPUs) are best suited for deep learning. In contrast, machine learning programs don’t need a higher computing power to function. They work seamlessly on most low-end hardware.
Deep learning systems require more time to train. This is due to the massive data the system must process and the numerous complicated math formulas and parameters it must solve.
Compared to machine learning, its training time is very long. It can take anywhere from a few hours to several years. Machine learning, on the other hand, will only require a few seconds to a few hours.
Machine learning algorithms will parse data in segments and combine them to produce the needed result. On the other hand, the deep learning program will assess the entire problem in one swoop.
Suppose you need to identify a specific object in a picture with machine learning; you must break down the process into several stages. However, with deep learning, you will only put the object in the system. Without any assistance, the program will produce the identified object and its location in the picture immediately.
Based on the above-mentioned differences, you must’ve realized that deep learning and machine learning systems have different applications. Some notable machine learning applications include email spam identifiers, predictive programs such as stock market prices and weather forecasts, and programs for creating evidence-based treatment plans in healthcare centers.
Deep learning applications include music streaming services, facial recognition, Netflix, and highly publicized self-driving automobiles. In self-drive vehicles, the multiple layers of neutral networks enable them to detect traffic lights and obstacles and determine when to slow down or speed up.
Challenges in Implementing Machine and Deep Learning Systems
Implementing both machine learning and deep learning systems has its fair share of challenges. Here are some of the hurdles you’re likely to face:
- Low-Quality or Insufficient Data: Both these AI concepts require training with relevant data on the topics they tackle to function effectively. Most users struggle to feed their AI algorithms with quality and sufficient data volumes. Unfortunately, the problem emerges when they lack access to the required data or if it is inexistent. This imbalance will result in discriminatory or discrepant results when using machine learning or deep learning systems.
- Outdated Infrastructure: These AI concepts must process massive amounts of information within seconds to produce much-needed results. Furthermore, they need suitable infrastructure with high-end operating devices to work effectively. Users with outdated machines can not fully benefit from these systems. For you to use machine learning or deep learning systems, you must invest in technologically advanced tools, applications, and infrastructure.
- Integration into Systems: Integrating AI into your training program requires more than plugin downloads in your LMS. You must first consider if you have the infrastructure, storage, and processors to run this system seamlessly. Also, your employees will need adequate training to use machine learning and deep learning systems. They will need the expertise to solve simple troubleshooting issues and to detect any underperforming issues by the AI algorithm. Setting up the necessary foundation to incorporate this technology is a significant challenge for most businesses.
- Inadequate AI Talent: Finding and hiring competent people with extensive knowledge and skills in these AI concepts is challenging. The lack of AI experts in companies is a great challenge, and it prevents them from implementing deep learning or machine learning in their organizations.
- Overestimating Your System: Technological advancements in machine learning, and deep learning have made some people believe these systems are never wrong. That is an overestimation of these systems because they heavily rely on data uploaded by the user. So, the data quality will determine the system’s accuracy and reliability. Many users have had negative experiences with deep learning and machine learning systems as a result of this problem.
- Cost Requirements: Implementing deep learning and machine learning systems will incur additional costs. Developing, implementing, and incorporating these systems will require collaboration with AI specialists to be successful. Besides paying these experts, you must fund AI training for your workers. Other inevitable costs include upgrading your IT equipment and infrastructure. Frankly speaking, this entire process is highly capital-intensive.
Machine Learning vs. Deep Learning: 7 Must-Know Facts
- Deep learning and machine learning have enabled robots to carry out journalism tasks which may, unfortunately, lead to the replacement of the staff who work in these related fields in the future.
- Deep learning has helped in personalizing user experience in the mobile application industry.
- Through deep learning algorithms, we are now able to create tools that can perform such tasks as language translation and speech recognition.
- Data scientists are working diligently to enable deep learning to be able to read characters and body language to detect emotions.
- Artificial intelligence combined with deep learning can process millions of information reports and use the data obtained to calculate the best possible outcome.
- Deep learning can now use computer vision to recognize and classify objects in images and videos.
- Through deep learning, it is now possible to design robotic pets that will look like real animals but will have the advantage of requiring less attention.
Machine Learning vs. Deep Learning: Which One Is Better?
As the deep learning and machine learning debate continues, it is clear that deep learning is emerging as the most popular option. Most people prefer it over machine learning for these reasons:
- Concealed insight from data: Deep learning systems trained using quality and big data can expose hidden relationships, patterns, and properties within the data that humans cannot detect. This model is suitable for gathering marketing, research, and business insights. It can also work with other informational sources like texts, images, and videos.
- Minimized Human Factor: Deep learning algorithms conduct feature extraction processes independently. They don’t rely on human intervention, yet they accomplish this task accurately. They also can complete this process faster and minimize the risk of human error.
- Unparalleled Efficiency: With sufficient computing capacity, deep learning algorithms can handle thousands of tasks in minutes. It also boosts its performance and efficiency by continuously training itself over time.
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