You must have heard the terms "Artificial Intelligence", "Machine Learning", and "Deep Learning" quite often, and that too interchangeably. However, for all those who aim to make a ground-breaking career in AI, it's imperative that you understand that all three are different.
Lexico defines AI as computer system theories and developments that perform activities that would otherwise (and usually) require human intelligence. Activities include but aren't limited to decision making, visual perception, and speech recognition. While you might have heard AI computer systems are smart, they cannot learn on their own. This hole is filled by machine learning and deep learning, and in today's article, we'll understand the difference between these two highly important concepts.
Machine Learning: Introduction
A type and subset of artificial intelligence, machine learning is a field where ML algorithms enable software applications to become more precise and accurate without the need to program them to do so. With the advent of machine learning, we witness present-day computer systems learn from the inputted data without the need to be continually reprogrammed. In simple terms, these systems constantly enhance their performance for any given task or activity. For example, when playing a chess game, you can choose a different level against the CPU (easy, moderate, hard, very hard). Here the system improves its performance without any human intervention or help.
One can leverage ML in various fields such as Finance, Healthcare, Art, and more. For educational purposes, there are different ways through which we can get machines to learn. Two prominent ways include:
- Basic Decision Tree
- Artificial Neural Networks
Deep Learning: Introduction
Deep learning is basically a subset of both machine learning and artificial intelligence. It’s a field that concerns itself with imitating the ways how humans gain knowledge. Experts consider DL an important aspect of data science that includes statistics and predictive modeling.Various well-known individuals and companies state (through experience) that deep learning will turn out to be machine learning's next frontier, i.e., the cutting edge of the cutting edge. The possibilities are you've already experienced the outcome of a deep learning algorithm even without knowing or realizing it.
If you have a Netflix subscription, I assume you've already viewed the recommendations it poses to you as to what you should watch. But, not just that, you might have experienced a streaming-music service that selects a song based on what your choices and selections were in the past and for which music you have hit the like or thumbs-up button.
Both the examples discussed are based on DL. However, a more prominent display of deep learning usage is Google's voice and image recognition algorithms that leverage deep learning. Just like ML is known as an AI type or a subset, the same is said for deep learning, i.e., it's a type (or a subset) of machine learning.
Machine Learning vs. Deep Learning: 5 Points of Differentiation to Understand
Human Involvement or Intervention
In the case of a machine learning system, human involvement is required, where an individual identifies and hand-codes the laid characteristics based on the data type. Whereas a deep learning system automatically tries to understand and learn those features, not requiring any human intervention or involvement. Let's take help from a facial recognition software example to understand this better. Here, the program first understands and learns how to detect and recognize face lines and edges and then hops onto other important parts of the face. Finally, it goes to the overall face representation. We must understand that the data usage in this is enormous. With the passage of time, as the program trains itself, the possibility of getting more accurate and correct recognition increases by notches.
Leveraging Hardware
Deep learning leverages a more robust hardware when compared to simpler ML systems. The reason for the same lies in the fact that a mammoth amount of data is getting processed and the mathematical calculation complexities concerned in the algorithms leveraged. A prominent example of hardware leveraged by deep learning is a GPU (graphical processing unit). On the other hand, machine learning systems run on low-end machines without requiring as much computing power.
Time Consumption or Requirement
By now, you have understood that in the case of deep learning, there are massive data sets as requirements, and there are various parameters and intricate mathematical formulas involved. Therefore, a DL system will require a lot of time for training purposes (a few hours to a few weeks). On the other hand, time consumption by machine learning systems ranges from a few seconds to a few hours.
Method or Approach Followed
In the case of deep learning, the systems view an entire problem in one fell swoop. On the contrary, in the case of machine learning, the systems parse data into parts, which are then combined to receive a solution or the result. Let's understand this with an example. Let's suppose you want a program to identify a certain object in an image, say a license plate on a car parked in the parking lot. Taking ML into consideration, you would require to undergo two steps: object detection and recognition. However, with DL, you just need to input the image, and with effective training, the program returns the identified object and its location in the image in one result.
Various Applications
It's believed that if you've gone through the differences mentioned above, you now understand that both these concepts have different applications.
Machine Learning
- Predictive programs (forecast stock market price)
- Email spam identifiers
- Evidence-based treatment plans for better medication solutions for patients
Deep Learning
Apart from the examples discussed in the earlier points (Netflix, music-streaming service, facial recognition), self-driving cars are another great example of deep learning systems. Here, the system leverages various neural network layers to perform actions such as determining objects to avoid, identifying traffic lights, and, most importantly, understanding when to speed up or slow down.
Final Words
Machine Learning and Deep Learning are the latest hot topics, and they are here to revolutionize customer experience, business automation, and how we live our lives. If you are an aspirant wishing to get into any of these domains, there's no time better than today. Being recognized as the best data science institute in Bangalore, Skillslash provides the best data science course with placement guarantee. With live classes led by industry leaders, be sure to learn these concepts in detail, and secure a high-paying job. Contact Skillslash’s support team to know more.
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AI has been the latest trend which is implemented across various sectors.
Its subsets Machine Learning & Deep Learning are also making huge impacts.
In this blog, we have discussed in detail about them.Learn More at :Digital Transfomation Companies in Chennai.
Artificial Intelligence (AI) is the tech buzz phrase of 2020.
Amazon Echo ShowThe current range-topping device with Alexa allows you to do everything from checking relatives in the next room or on the other side of the world to buying pants from Amazon to stay away from your daily schedule.The 7-inch screen allows you to make video calls to other show users, which is great, but can also get a brief peak, for example, the appearance of an elderly relative (with their permission, of course) should disturb them.
Great for peace of mind.Not only does Amazon’s Alexa AI provide you with music based on advanced voice search terms — “play sad pop songs from the 1980s,” for example — it also learns to understand you better, the more you use it.2.Kuri Home RobotAlthough Kick starters and Prototypes have come and gone, we haven’t seen many Home Assistant robots come into the mainstream.
With the advancement of AI technology and reduced hardware costs, it may be time for robots to truly be useful around the home, and Kuri is leading the charge.Music can be played by these robots, take photos and videos on the fly, answer your questions, and engage in some conversation, thanks to the AI smarts hidden behind its mechanical eyes.
The gadget also includes mapping sensors, which allow the Kuri to move around the dining room tables and chairs in the living room.Two other areas where the face and voice recognition robot’s artificial intelligence features take effect, so Kuri knows who is talking and can change its responses.To read about: The Revolution Of Artificial Intelligence TechnologyYou can configure Kuri via the affiliate app (to wake you up in the morning), as well as review photos and videos that Kuri has captured while traveling around your home.
By learning when and how to use your heating, the Nest Thermostat can help you use your heating more economically and efficiently.Allow it to use your phone’s location, and the nest automatically turns off heating when other members of your home are gone.
Let’s look at the concepts that dominate conversations about machine learning and deep learning and how both are different from each other.Read Full Article: https://bit.ly/3hPin7V
Recent developments in the field of training Neural Networks (Deep Learning) and advanced algorithm training platforms like Google’s TensorFlow and hardware accelerators from Intel (OpenVino), Nvidia (TensorRT) etc., have empowered developers to train and optimize complex Neural Networks in small edge devices like Smart Phones or Single Board Computers.
This has led to a profusion of initiatives to use such trained models in the domain of Health and Safety (HSE) at workplace.
The output of the model can be used to send notification or drive other processes.The following are some of the interesting initiatives that we are working on training computer vision models to improve health and safety in the workplace.Our Initiatives:PPE (Personal Protective Equipment) Compliance Improper adherence to Personal Protective Equipment in industries is a big problem.
Employees don’t wear PPE either due to lack of knowledge on safety or due to the perceived inconvenience in wearing PPE.
We have assembled a detailed data set of workers wearing different PPE and have trained models that can accurately detect if the required PPE is worn by an employer or not.
The environment is very harsh and corrosive, and the truck drivers need to be very careful and diligent in following the sequence of procedures to unload the cargo.
