Difference between Machine learning and Deep learning

 

Machine Learning

Machine learning is the science of making computers work according to algorithms designed and programmed. Many researchers believe machine learning is the best way to move towards human-level AI. Machine learning includes the following types of patterns

- Supervised learning pattern

- Unsupervised learning pattern

Deep Learning

Deep learning is a subset of machine learning in which related algorithms are based on the structure and functions of the brain, called artificial neural networks.

The whole value of intensive learning today lies in the supervised learning or labeled data and algorithm learning.

In deep learning, each algorithm goes through the same process. It includes a hierarchy of nonlinear transformations of input data that can be used as output to build a statistical model.

Consider the following steps that define the machine learning process.

1. Identifies relevant datasets and prepares them for analysis.
2. Selects the type of algorithm used
3. Creates an analytical model based on the used algorithm.
4. Trains the model to a set of test data, modifying it as needed.
5. Runs the model to obtain test results.
   
   

Difference between ML and DL

Amount of data

Machine learning works with large amounts of data. It is also useful for small amounts of data. On the other hand, deep learning works well when the amount of data is growing rapidly. The following diagram shows Machine Learning and Intensive Learning with a volume of data 

Hardware dependency

Deep learning algorithms rely heavily on high-performance machines as opposed to traditional machine learning algorithms. Deep learning algorithms perform multiple matrix multiplications, which requires the support of a large amount of hardware.

Functional engineering

Feature development is the process of adding domain knowledge to specific knowledge to reduce the complexity of the data and make visible patterns for the learning algorithms that work.

Examples - Traditional machine learning patterns focus on pixels and other features required for the feature development process. Deep learning algorithms focus on high-level data functions. This minimizes the task of developing new feature extractors for each new problem.

Approach to problem-solving

Traditional machine learning algorithms follow a standard problem solving procedure. It breaks the problem down, solves each of them, and combines them to get the results you want. Deep learning is about solving problems, not separating them from start to finish.

Time of completion

Execution time is the time it takes to train the algorithm. Deep learning takes a long time to learn because it involves many parameters that take longer than usual. Machine learning algorithms require relatively short execution times.

Rationality

Interpretability is a major factor when comparing machine learning and deep learning algorithms. The main reason is that deep learning is still receiving special attention before its use in industry.

SASVBA

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SASVBA has an excellent infrastructure for machine learning and deep learning with newly developed labs that enable trainees to efficiently complete multiple courses. We provide machine learning certification training and deep learning certification training annually to thousands of students from all over the world. Each trainee is provided with personalized training according to their needs and requirements.

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