Difference between Machine Learning and Deep Learning
A machine’s ability to mimic human behavior is ARTIFICIAL INTELLIGENCE (AI). Machine Learning (ML) is a subset of AI, & Deep Learning (DL) is a subset of ML.
ML provides systems the ability to automatically learn from experience without being explicitly programmed.
Examples: Spam email detection, Online fraud detection, YouTube video recommendations
DL is ML which is capable of learning unsupervised from data that is unstructured or unlabeled.
Examples: Self driving cars, Chatbots, DL Robots
The primary difference between the two is the way we feed data to each. For example, to train an ML model on what a square is, we need to manually define the properties of a square like length, angle, etc. On the other hand, to train a DL model on the same, we just need to expose it to several images of different types of squares. DL models learn the features by themselves.
In ML, even with the best feature specifications, it simply isn’t possible to grasp the complex patterns in the real world data. DL overcomes this limitation in ML. DL is very similar to how the human brain learns new concepts by being exposed to new data.
|Hardware Requirement||Low end machines will suffice||Large storage, Advanced algorithms, High end computational power, High performance GPUs|
|Input Data Type||Structured, Labeled||Raw|
|Size of Input Data||Can easily work with small amounts of data||Doesn’t perform well with small amounts of data|
|Models’ Training Time||Few seconds to few hours||Weeks|
|Decision Making||Takes a decision and also gives us the reasoning||Results can be similar to human work, but it won’t tell us why|
– Jothi Abarna