Machine learning Vs Deep learning Vs Reinforcement learning
If you are just starting your journey into the most hottest field right now -Machine Learning, then you must have heard of these confusing words — Deep learning, Reinforcement learning, Supervised learning, Unsupervised learning.
Oh man! Here come the old topics that I had been learning ever since I started machine learning.
But the thing that I found with new comers is that they often get confused by definitions of these terms.
They have questions like below:
- Is machine learning and artificial intelligence the same?
- What is the difference between machine learning and deep learning?
- How is supervised, unsupervised learning related to machine learning and deep learning?
After you complete reading this post to the end, I am pretty sure that you will never get pop up questions like the ones above.
Artificial Intelligence and Machine Learning
First of all, let me tell you this — AI and ML are not the same.
We use machine learning to give intelligence to the machines(computers).
There are two ways by which we can give artificial intelligence to machines:
By Rule-Based Programming or by using Machine Learning.
In machine learning, we let the machine learn by itself.
Ok, let me make this clear:
How do you learn for your exams?
You read the textbooks provided by your school/college and learn from the contents in the textbooks and thus you can perform well in your exams.
Similarly, you provide the machines(computers) with a dataset and the machine learns from the dataset and does the prediction.
Be Alert! Examples Ahead.
Okay, just think like this: you are a machine learning research scientist at a healthcare startup. You are asked to create a machine learning model to predict whether a person has leukemia or not by analyzing his/her blood sample.
So, generally what you as a research scientist will do is that you will train(teach) your model by giving it a huge dataset which contains images of blood cells(previously analyzed by human doctors) with corresponding label as leukemic or non-leukemic.
After training, the model must have understood the patterns for leukemic and non-leukemic cells. Now if you give an image of blood cell collected from a new person, it should be able to predict the right answer(leukemic or non-leukemic).
Now the machine is intelligent enough(like an expert doctor) to identify a leukemic cell from the non-leukemic.
Deep Learning
Deep learning is similar to or we can call it as a subset of machine learning.
The method for deep learning is similar to machine learning(we let the machine learn by itself) but there are a few differences.
Some of them are:
- Algorithms used in deep learning are generally inspired from human neural networks.
- Deep learning requires huge datasets and computational power(you guessed it right -GPU’s) than machine learning.
So, when ever you use an algorithm which has a word -Neural Network, or when ever your algorithm requires a huge dataset to learn -Yo man! You are using the hottest topic of the present world, deep learning.
Reinforcement Learning
The general overview about this topic that I can give you is that -Rewards and Punishments.
Oh! What the heck is that?
Ok, let me ask you a question.
How did you learn to walk in your childhood?
Through Trial and Error, right?
You will try your maximum to walk without falling -falling down while walking is a punishment and walking without falling is a reward.
So, what will you do? You will try to minimize the punishment(falling down) and maximize the reward(walk without falling down).
Have you ever heard of Google’s Deepmind?
Deepmind created a robot named AlphaGo which uses this technique(reinforcement learning) to play the most difficult board game — Go.
AlphaGo beat the world champion in Go. Sounds amazing, right?
Get ready to get really amazed.
Deepmind created another version of AlphaGo named AlphaGo Zero which beat AlphaGo hands down(100–0 or something like that).
The field is just in it’s budding stage, this is the right time to play with this cool stuff. Just keep exploring this wonderful field of machine learning.
Conclusion
In the next post, you will get to know about supervised and unsupervised learning and how they are related to machine learning and deep learning stuff.
