#1 What is Deep Learning?
Machine Learning (ML): It is a way of teaching machines to solve problem using data, rather than doing explicit coding for each situation.
Deep Learning (DL): It is a part of Machine Learning which tackles any problem (in theory) using interconnected nodes (neurons / weights & biases). It is highly flexible, adaptable and even mimics human brain.
Types of Machine Learning:
- Supervised Learning - consumes data with proper labels
- Semi-supervised Learning - requires partial data
- Unsupervised Learning - works by clustering similar data
- Reinforcement Learning - based on environments, policies, rewards
History of ML
- AI winter: It is period of time where AI was has having a lot of attention but no one ever took it seriously (implementation).
- Big Data: In 80’s and 90’s data was produced but due to limited computational power and low quality of data, AI never took off.
- Technology: Now we have very fast accessible computational power, good quality big data, and improved algorithms.
Limitations of Machine Learning
- Model requires some amount of good data
- Data collected should also contain labels
- Model learns only patterns which exist in the dataset
- Model only predicts outcomes of the classes, not recommended actions.
- Feedback loops: Few models are biased towards a region of the dataset, when the model retrained on the new data, It tends to go in a loop where model gets more and more biased