Deep Learning vs. Machine Learning – Which One Is Better?

Image by Gerd Altmann from Pixabay

Machine Learning and Deep Learning are similar but, at the same time, different. To further explore, we need to precisely inspect every aspect of them to discover which one is better or if they both are useful for different fields.

Deep learning – Interestingly, Deep Learning is a subset of machine learning or, in other words, a subset of Artificial Intelligence. This field shows computers to learn by example. Deep learning takes different algorithms and neural networks that are made to copy human brain functions.

Machine Learning – It represents a division that is founded on the idea that systems can take everything from data and learn from it in order to recognize patterns and make conclusions with slight human interference.

Deep learning as a fragment of the machine learning approach

Many technology-related people think that deep learning is present only in the research phase, testing capabilities, but they are wrong. It is already a part of a machine learning approach. For example, it is already present in speech recognition and voice patters, as same as image recognition and automatic image captioning and more. One worldwide known example comes directly from Netflix and its recommendation system.

If one looks at deep learning as part of machine learning toolset, one sees that it not only solves problems and acts but also solves the problem by itself. The success is quite spottable because outdoes people in responsibilities like the classification of objects in images.

Shortly, deep learning is a specific practice of machine learning that does end-to-end learning where raw data comes in a network and receives a task in which the system learns how to accomplish anything automatically. It has been estimated that one necessary separation comes from machine learning and deep learning. To specify, the algorithms that deep learning uses succeed as the size of data rises, and feature withdrawal is automatic. That needs a GPU that performs highly and large amounts of labeled data.

Deep learning and Machine Learning Algorithms differentiation

Indifference from traditional computer algorithms, the algorithms that machine learning is using combined with statistical models fix specific tasks and lack instruction.

Machine learning algorithms include:

Managed machine learning algorithms – a model that includes predictions founded on evidence made by regression and classification techniques.

  • Linear regression
  • Polynomial Regression
  • Decision Trees
  • Random Forests
  • Unmanaged machine learning algorithms – notice patterns in data by drawing assumptions from unlabeled input data.
  • Clustering
  • Dimensionality decrease
  • Association analysis

Reinforcement Learning, on the other hand, is a set of algorithms that includes software agents to act on the environment to increase the values of cumulative reward. Besides the Markov decision process it includes:

  • Principle of optimality
  • State-value purpose
  • Monte Carlo approaches
  • Sequential difference approaches
  • Direct policy exploration

The algorithms in deep learning can be named as “layers” of the artificial neural network and they pass a representation of the data to the next layer. Deep learning algorithms include:

  • Back-propagation
  • Stochastic gradient descent
  • Learning rate decay
  • Dropout
  • Max pooling
  • Batch Normalization
  • Long Short-term Memory
  • Transfer learning

The Advantages

Machine learning is required in businesses that want to comprehend the relations of mechanisms in data and when it the right time to clarify and infer the research results. Deep learning can work with specific types of data, like videos, audios, and text information.

Machine Learning & Deep Learning – Goals and Purposes

Everything depends on the industries, or to be more precise, the practical goals of machine learning are driven by them. For example, they are signified in these groups:

  • Computer vision
  • Control systems
  • Obtaining information
  • Marketing
  • Medical diagnostics
  • Internet Advertising
  • Natural language processing

The Future

The future is bright for machine learning and deep learning for sure. The modern environment requires to implement these two in different industries. It can make one company significantly better than the competition.