Machine Learning:
ML is Subset of Artificial Intelligence. It is a tool by using which we are going to Copy or Mimic Natural Intelligence. That focuses on the development of algorithms and statistical models that enable computers to learn and make predictions or decisions without being explicitly programmed. It involves training algorithms on large datasets to identify patterns and relationships and then using these patterns to make predictions or decisions about new data.
when ever we going to finding a function we use statistical concept. Machine Leaning allows the computers to learn from the experiences by its own, use statistical methods to improve the performance and predict the output without being explicitly programmed.
y=f(x) , here y = output , x = input , f = function
it shows the relationship of input’s and output’s and gives the best fit function.
How does Machine Learning work?
The working of machine learning models can be understood by the example of identifying the image of a cat or dog. To identify this, the ML model takes images of both cat and dog as input, extracts the different features of images such as shape, height, nose, eyes, etc., applies the classification algorithm, and predict the output. Consider the below image:
Deep Learning:
DL is subset of ML and AI. It works technically in the same way as machine learning does, but with different capabilities and approaches. It is inspired by the functionality of human brain cells, which are called neurons, and leads to the concept of artificial neural networks. It is also called a deep neural network or deep neural learning.
when ever we going to finding a function we use Logical Structure.
How Deep Learning Works?
We can understand the working of deep learning with the same example of identifying cat vs. dog. The deep learning model takes the images as the input and feed it directly to the algorithms without requiring any manual feature extraction step. The images pass to the different layers of the artificial neural network and predict the final output.
Main Difference between ML and DL:
- In ML the function can use Statistical Concept but in DL function can use Logical Structure.
- When we have less amount of data then we use ML. Because if the amount of data has increases the performance also increase but once upon a stage the performance not increases and it will be stable.
But when we have a large amount of data then there we will use DL. If data increases in DL the performance should also be increases. The below graph shows the how performance increases.
3. In ML Training time is Less, But in DL Training time is High.
4. In ML Hardware requirements are CPU’s , But in DL High performance Hardware are required like GPU’s / TPU’s.
5. ML is easy Interpretability, But DL is very very difficult to Interpretability.