Source: Deep Learning on Medium
As you may know, the advancement of Artificial intelligence has been hyped i.e. machine Learning and Deep Learning. Anyone interested in this field, constantly freezing their minds with these terms, whether they understand these terms or not. ML & DL may be fascinating but not at all easy for a beginner. So before choosing your career in ML & DL, just interrogate enough about how both works and what kind of skill sets are required. The distinction between machine learning (ML) and deep learning (DL), for example, can be a bit confusing to the uninitiated, but it makes all the difference for companies trying to harness the reams of data they collect, notes this opinion piece by Adam Singolda, CEO and founder of Taboola.
I hope you’ve heard this, many times! and that is fascinating also-
Hey mate, how did you do that? He replied: Machine Learning. ( sounds easy right? well no.). Don’t jump to a conclusion without even knowing the depth of it. Ok, let’s proceed further.
How do you define Machine Learning :
Well, you know it’s a subset of AI but wait what about the process involved in ML. The key insight lies within algorithms, model training, and concepts that may not seem easy to you like you think. “ Machine Learning can be defined as a science of making computers act and learn like humans and enhance their learning capacity over time in independent fashion by feeding them with information and data in the form of real-world interactions and observations.”
A simple example would be Netflix, which uses an algorithm to learn about your preferences and present you with the choices that you may like to watch.
In the case of machine learning, the algorithm needs to be told how to make an accurate prediction by providing it with more information, whereas, in the case of deep learning, the algorithm can learn that through its own data processing. It is similar to how a human being would identify something, think about it, and then draw any kind of conclusion.
Now, what about Deep Learning :
Deep learning is a subset of machine learning which is based on learning data representations, DL is inspired by the function and structure of the brain known as Artificial Neural Network. Deep learning acquires tremendous flexibility and power by learning to display the world as a fixed hierarchy of concepts that are defined concerning simpler concepts, and more abstract delegations calculated based on less abstract ones. Although the term deep learning has been spoken for years now, these days with all the hype, it is getting more attention. “Deep Learning is Large Neural Networks.”
Neural networks are a set of algorithms, modeled after the human brain. They are sensors: a form of machine perception. Deep learning is a name for a certain type of stacked neural network composed of several node layers. Each layer’s output is simultaneously the subsequent layer’s input, starting from an initial input layer.
Deep-learning networks are distinguished from the more commonplace single-hidden-layer neural networks by their depth; that is, the number of node layers through which data is passed in a multistep process of pattern recognition. Three or more including input and output is deep learning. Anything less is simply machine learning.
Finally, ML vs DL :
Do you know, Machine Learning almost always needs well-structured data for processing, but Deep Learning depends on layers of Artificial Neural Network (ANN).
Deep Learning is banging the application, for now, We all mostly depend on DL when it comes to performance, the lower failure rate is what makes DL more popular, and yes it should be. And no wonder, if you are also thinking to learn Deep learning in more depth, well that I will add next time, but as a beginner, if you are aware of these trends, congrats ! you will follow the right direction.
WHAT ARE THE LATEST TRENDS IN MACHINE LEARNING AND DEEP LEARNING? :
As machine learning and deep learning go hand-in-hand, here are some of the trends that we are most likely to see shortly (or see better versions of the same):
(1) Transfer learning. (2) Better approaches in cybersecurity.
(3) Robotic process automation. (4) Transparent decisions.
(5)Edge intelligence. (6) Quantum computing. (7) Cloud computing.
It’s never too easy to learn AI, ML or DL. Keep in mind, it’s only your dedication, practice, patience that decides your potential. Don’t panic if you don’t understand any concept, you can always discuss it.
We have studied Deep Learning and Machine Learning and also looked at a comparison between the two. We have also looked at images for better representation and understanding. Now, you know that numerous fine nuances divide machine learning and deep learning, although both are tied to similar principles of AI. But the crux is that the former includes more complex code while the latter leads to more enhanced results.
Happy Learning Fam!