What is Machine Learning?
Machine Learning (ML) is a branch of Artificial Intelligence (AI) that provides the ability to the systems to learn and improve automatically from experience without being programmed. It mainly focuses on developing computer programs to access data, use it and learn from it.
What is Deep Learning?
Deep Learning is a function of Artificial Intelligence (AI) that imitates the human brain’s thinking in processing data and creating patterns to use them in the decision-making process. Deep learning is also known as deep neural learning or deep neural network.
- Differences between Machine Learning & Deep Learning
- What is Machine Learning
- What is Deep Learning
The Key Differences between Machine Learning and Deep Learning
Machine Learning deals with the definition of a problem, the analysis of that problem, and creating a solution, while Deep Learning deals only with creating a product, service or idea, all at the speed of human thought. These fundamental differences will be significant in the context of Artificial Intelligence.
The key differences between Machine Learning and Deep Learning are based around the core idea of supervised or unsupervised learning. In supervised learning, the goal is to create a function or solution that can be tested and re-tested repeatedly without changing the initial labeled data or the original training data.
The reason why this is important to understand is that supervised learning requires the data to be labeled correctly to achieve results. Training data does not need to be changed when making changes to the system. Deep learning, on the other hand, is all about labeling the data without any supervision.
When it comes to producing new data, deep learning systems can achieve results much faster than a supervised machine learning system. This is because the deep learning system is not concerned with creating new data but only validating the old data that was already labeled.
This makes the training process much more manageable. Also, because a supervised system must use new and labeled data in order to test and retest, it is much more expensive. Deep Learning, on the other hand, can scale much better when used with existing quality data.
However, the biggest difference between these two technologies lies in their underlying mathematical algorithms. Deep Learning utilizes an algorithm called the Recurrent Neural Network (RNN). Machine Learning uses an algorithm called the Searching forano- Algorithms (SANN).
These two algorithms are entirely different, yet both are used in most, if not all, Machine Learning systems. One is specifically designed to train a network by discovering patterns from data, while the other is designed to detect wrong or missing classifications. These two differences in the algorithms profoundly affect how effectively a Machine Learning system can be used.
With traditional machine learning, you would typically need to tweak the program code to correct wrong classification errors, proving to be a very tedious task. Also, because a mathematical algorithm produces classification errors, there is no possibility to catch wrong inputs by manually proofreading the program code.
These two factors, along with the fact that deep learning requires large amounts of data that can tolerate misspellings, mean that it has much higher false precision than conventional machine learning.
The biggest factor that leads to the vast differences between the two methods lies in the structure of the neural networks that both utilize. Deep Learning systems use large input spaces structured in terms of every possible feature that a user can input into the system.
These features include words, images, text, and other forms of data. Machine Learning uses smaller training data spaces that are less structured. The programs it outputs can contain potentially miss-pelt words, but because these classes are never miss-pelt in a neural network, they are not corrected during the same process.
Thus, Machine Learning often produces better results than Deep Learning. Conventional Machine Learning programs include pre- trained databases which contain labeled data for all sorts of things. These learning types include speech recognition, language translation, and image processing, and so on.
Conclusion
Deep Learning often requires the developer to build their neural networks from scratch in order to achieve results. Although this process takes longer, it is much more efficient, and therefore Machine Learning is currently the preferred method of learning in most modern industries.
Machine Learning often includes the ability to design and create its own algorithms, so the programmers who use these programs have far more control over how the system will function and what it will do.