Before hitting our head to understand the difference between AI vs ML vs DL. Lets discuss a short story about a random family members GRAND FATHER, FATHER and SON.
One fine day, the Son bought new iPhone and gave to his Grand father just for a particular time. Grand father doesn’t aware of how to operate it, but finally he managed to open the camera and took a picture. Son came back and he was happy that Grand father was able to operate at least the cam with his tech knowledge then he gave that phone to his father to check on his operating skills. The Father took some time and he was able to make calls and also took some selfies with different angles. Son was happy to see his fathers engagement towards tech. Son have all the skills to operate the iPhone end to end.
– iPhone and the family –
Moral of the story
From the story, we understand that Son’s knowledge on using iPhone is more than Father’s and Father’s knowledge is better than Grand father’s. Also we cannot compare them since they are from different age groups and they lived different timelines of technology.
Similarly AI, ML and DL cannot be compared as they are subset. DL is the subset of ML and the ML is subset of AI. So here the Grand father is Artificial Intelligence, its algorithm mostly predicate nor propositional. The Father is Machine Learning, takes multiple training data set to find the better feasible solution. The Son is Deep Learning who have the latest skill sets to find the accurate solutions using the neural network. Now it may be simple for you to understand further, Lets jump on to the techie background of AL, ML and DL to know how they deliver solutions.
Understanding Artificial Intelligence
The Goal of AI includes learning, reasoning and perception, many people thinks that the AI means developing a robot with human intelligence but that is not the only case.
AI’s are created using the logics, based upon the perception of the logic it will act upon it with its knowledge base information. The main principle of the Artificial Intelligence is the learning and problem solving. Learning is the processes of observing the environment and train the algorithm for minimizing the errors.
Machine learning vs deep learning
Machine learning needs more dataset and time to train the algorithm. The more data it takes as input for the algorithm the better it can deliver the accurate result.
Deep learning creates an artificial neural network, it is more accurate compared to ML since its the subset of ML. In Bigdata field, deep learning plays a major role.
Comparison factors
| Factors | Machine Learning | Deep Learning |
|---|---|---|
| Data Requirement | Can train on lesser data | Requires large data |
| Accuracy | Gives lesser accuracy | Provides high accuracy |
| Training Time | Takes less time to train | Takes longer to train |
| Hardware Dependency | Trains on CPU | Requires GPU to train properly |
| Hyperparameter Tuning | Limited tuning capabilities | Can be tuned in various different ways |
