Understanding the most recent progressions in artificial intelligence can be overpowering. So it would be better to learn the rudiments of AI if you are new to this subject. Numerous AI advancements boil down to two ideas: machine learning and deep learning.
For many people, both of these terms are used interchangeably more often in the AI world. Notwithstanding, that is false. Every individual who looks to comprehend information in the field of artificial intelligence should start by understanding its key concepts and their disparities. We should jump into the blog to know precisely about machine learning and deep learning along with their ins and outs and most importantly, their differences concerning each other.
What is Machine Learning?
Machine learning is a part of artificial intelligence that involves calculations to parse information, carve outcomes from that information, and afterwards, apply what’s figured out for making proof-based decisions. Machine Learning is used by nearly every automatic device or system in numerous business niches. Every ML algorithm gets updated continually by learning continuously about responses and user behaviours.
Machine learning is based on many complex math problems and codes, which is the main factor for all the mechanical capacity of ML-based models. If a model is based on “machine learning”, it simply implies that it will execute various calculations in the background to process the information and data that will be continually improving over the long run. Following are some points to summarize machine learning:
- Consider machine learning as a crossroad of computer science and statistics that enable systems to learn without any sort of explicit programming.
- All of the machine learning problems are categorized into two broad categories, namely supervised and unsupervised.
- All of the machine learning algorithms get modified regularly based on daily learning and data inputs.
How does machine learning work?
In simple words, machine learning enables computers to think like humans; learning and evolving based on previous experiences. It is based on exploring information and recognizing patterns along with a little bit of human interference.
Practically every operation that requires data-defined patterns or utilization of various rules can be achieved with machine learning by making the process automated. This permits organizations to automate processes that previously require only humans to handle repetitive tasks such as client assistance, accounting, etc. Machine learning utilizes two principal methods:
1. Supervised learning
This permits individuals to gather information or produce an information yield from past ML deployments. Supervised learning is efficient because it works similarly to the way humans learn. In this, the PC is presented with an assortment of labeled information known as training sets.
2. Unsupervised learning
Unsupervised machine learning assists you with discovering a wide range of unknown information in data. Here, algorithms attempt to get familiar with some innate structure to the information with just unlabeled models. Two regular tasks for unsupervised learning are clustering and dimensionality reduction.
In clustering, we endeavour to cluster information focused into significant groups to such an extent that components inside a given cluster are similar to each other yet different from components in other clusters.
What is Deep Learning?
Deep learning is considered another segment of AI. Some of the most globally popular technologies such as Google’s voice recognition and image recognition are based on deep learning. Similarly, as machine learning is viewed as a part of AI, deep learning is frequently viewed as a part of machine learning—some consider it as a subset. While ML utilizes less difficult ideas like predictive models, deep learning utilizes artificial neural networks intended to emulate how humans think and learn.
However, similar to machine learning, these models feed on data that is comparatively more gigantic for better understanding and accurate outcomes. The artificial neural networks ask a series of binary yes or no type questions based on the data received, and also make the use of certain mathematical calculations and segregate the data based on the answers received.
- Deep learning is a subset of machine learning.
- Deep learning is based on layered structures of algorithms known as artificial neural networks.
- Deep learning requires a huge amount of data but minimal human interference.
How does deep learning work?
Deep learning models are intended to constantly inspect the information by making the use of logical structures for making outcomes just like humans. To accomplish this, deep learning applications utilize a layered structure of algorithms known as artificial neural networks. The functioning of these neural networks is inspired by the human mind, which makes them more efficient than other models.
It’s a precarious possibility to guarantee that the deep learning model doesn’t make inaccurate conclusions—like different AI models, it requires heaps of training for getting the learning measures right. Yet, when it fills in as it’s planned to, utilitarian deep learning is frequently considered to be a robust backbone of artificial Intelligence.
Machine Learning vs Deep Learning
The basic difference between machine learning and deep learning is that deep learning is a subset of machine learning. For more clarity, consider deep learning as an evolution for machine learning. Following are some of the factors based on which both of them are differentiated:
1. Human interference
Machine learning models require a lot of human intervention for the identification and execution of and hand-codes for the operations based on data and information such as pixel value, shape, orientation, etc. However, deep learning models figure out such kinds of details without various human interferences. Taking the case of face recognition, for instance, it first learns and examines all the face details such as tracking edges, eyes, nose, etc., and eventually learns the complete face appearance.
Indeed, the data input is gigantic but with the passing time, the program increases the scope of accuracy and starts giving perfect answers. All of these training processes become possible due to neural networks. The best part is that deep learning doesn’t require humans to give tons of inputs and continuously recode the program or manually feed the data.
2. Hardware required
For the successful processing of huge amounts of data sets and execution of complex mathematical calculations used in algorithms, every deep learning model has a prerequisite of running on robust and expensive hardware. One such hardware commonly used for deep learning is the graphical processing unit (GPU).
On the other hand, machine learning doesn’t require high-end data and complex equations to solve; therefore these models can also run on low-end and inexpensive machines that don’t have high computing power.
3. Time consumed
Again, because of the huge data sets used by deep learning systems, they take more time to get trained and give more accurate results as compared to machine learning. And since deep learning considers and analyzes various data points along with solving complex mathematical equations by itself, it consumes way more time.
Whereas, all the models based on machine learning consume less time; maybe seconds or minutes to get started successfully and provide acceptable outcomes.
Machine learning algorithms compute the data in bits and further combine that data for carving out the solutions or outcomes. Whereas deep learning algorithms take the complete problem as a whole for processing the operations.
For example, programming for the identification of certain objects in images (determining the objects, location, checking license template, etc.) would only require two steps in ML models: Object detection and object recognition. But in deep learning, the complete image will be taken as an input, and based on extensive training and stored data, the output will be obtained as an accurate result.
5. Interpretation of result
Interpretation of results for various operations is simple in machine learning models. This is because their interpretation is quick and easy but is not much accurate and refined as compared to deep learning.
On the other hand, interpreting similar problems with deep learning gets complex because deep learning models strive for accurate results and that only comes by using lots of data and inputs.
6. Data dependency
Although a lot of data helps machine learning to yield better results, this model can also work efficiently with a smaller amount of data. The major prerequisite for machine learning models is structured data form.
However, deep learning algorithms are only and only dependent on huge data for processing the conclusions. But the good thing is that they can work on the execution of both structured and unstructured data because of the utilization of artificial neural networks.
7. Feature engineering
In 2022, it can be said that the machine learning model does need some modifications and advanced features as well for meeting the ever-evolving market needs. Since deep learning is the enhanced version of machine learning, it doesn’t require any significant modifications. In addition, deep learning also learns all the high-level features by itself by simply utilizing the data and regular inputs from users.
Deep Learning vs Machine Learning: Head-to-Head Comparison Table
|Machine Learning||Deep Learning|
|ML algorithms are utilized for parsing and analyzing the data for carving out proof-based conclusions of certain operations.||Deep learning algorithms are structured in layers known as “artificial neural networks” through which more correct and informed decisions are made.|
|Models based on machine learning require lesser time for training and utilize thousands of data points.||Deep learning requires significant time for training and uses millions of data points for providing perfect outputs.|
|The outputs are in numerical forms such as classification of the score for applications.||The outputs can be in any form, ranging from free form elements to free texts and even sounds.|
|Comes with a restricted tuning capacity.||Deep learning models can be tuned in lots of other ways as well.|
|Machine learning is considered a superset of deep learning.||Deep learning is a subset of ML.|
|Utilizes a lot of algorithms that automate all the model functions and predicts all the future takeaways from the data.||With the help of neural networks, these models run all the inputs through various processing layers for interpreting results.|
|Human intervention is required in processing algorithms for analyzing target variables from the data set.||Every algorithm in deep learning is self-dependent and they modify themselves by learning regularly right after the production.|
|Machine learning is used for automating regular and repetitive tasks such as providing customer support.||Deep learning is used for solving complex operations of organizations and new technologies.|
|It can be trained using the Central Processing Unit (CPU).||It can be trained using the Graphics Processing Unit (GPU).|
Both machine learning and deep learning have infinite potential in the future because of the value they add to the day-to-day process of organizations. With the advancements in technology and the exponential increase in the adaptation of automation and robots, their usage becomes a crucial factor for a lot of organizations and improves the daily work process in both small and big ways. Their scope has touched nearly every sector including the healthcare sector by helping doctors to detect life-threatening diseases such as cancer in early stages.
In the financial sector, machine learning and deep learning-based models are helping organizations to save more money by investing tactically along with utilizing resources. However, it will not be wrong to say that we are still at the early stages and certainly a lot of things will be revolutionized in the coming decades.
The terms machine learning and deep learning are used interchangeably by a lot of people, which is wrong. Both of them have different grounds and functions in different ways, however, they both come under one single roof, which is known as artificial intelligence. Both of them are used for solving different problems by utilizing multiple datasets in general.
Hopefully, the detailed information about machine learning and deep learning provided in this blog helped you develop a good understanding of them. Also, you may now have gained clarity about how both of them still being a part of the same technology are yet noticeably different from each other.