What do we mean by deep learning?
Deep learning is a form of artificial intelligence based on machine learning.
To understand the “deep learning” model, we must first look at the “machine learning” concept introduced by Alan Turing in the 1950s. This English mathematician invented a so-called Learning Machine capable of memorizing and learning new notions. These machines have evolved in step with technological advances, and many algorithms have emerged.
Inspired by this learning model, deep learning takes the approach to the next level by incorporating artificial intelligence.
Understanding deep learning principles
In a deep learning approach, data is analyzed continuously. This strategy makes it possible to design a logic structure within the algorithm that mimics human reasoning.
To enable this, deep learning applications use a multilayer structure, called an artificial neural network, inspired by the network of biological neurons in our brain. Thanks to this model and inspiration, learning is fast and effective.
Machine learning analyzes the collected data and learns it.
Deep learning is a subfield of this broad category. Multilayer algorithms are used to create an artificial network of neurons that functions in a similar way to the human brain.
What are the pros and cons of this learning method?
Deep learning is driving progress in big data science by analyzing and assimilating vast quantities of information.
This approach offers some major benefits:
- The algorithm runs on a 24/7 basis
- The training provided is high quality and requires less (text, image or videos) content
- This model is quick and easy to implement.
- Its intuitive interface makes it easy for learners to use
- Defects and problems are identified in seconds, allowing quality training to be provided to students
- Technical support is available, including access to a network of engineers and experts.
However, building an effective model requires a large volume of data. This can therefore be a time-consuming operation.
Deep learning is revolutionizing learning methods and e-learning courses. As with other AI-based models, some of the data may be wrong. To rectify this, multiple courses must be created before the model is perfected.
