Understanding the difference between machine learning vs deep learning helps to better appreciate the concept of artificial intelligence. These are two technologies that make machines “intelligent”, that is, capable of coming to decisions autonomously. The two terms are sometimes used as synonyms but, although they have some features in common, they maintain substantial differences.
The concept of machine learning began to develop in the 50s, where deep learning is, in fact, a derivative of machine learning. In what follows, we analyze these two concepts to better understand the chief differences.
The concept of machine learning
machine learning is a technology that, after receiving human feedback, uses a certain algorithm based on structured data to then catalog and analyze subsequent data and program activities. In this case, human intervention is more active since, after the initial phase, the algorithm is managed and optimized by human feedback, which indicates to the system the exact categorizations and the wrong classifications.
In practice, with automatic learning, the algorithm is “educated” and “trained” in such a way that it can learn in the different contexts in which it operates. To work effectively, machine learning needs a large amount of data to adapt to the various situations that occur, responding to external inputs and extracting useful information from the data.
An example of machine learning is artificial vision, that is the ability of a machine to recognize and identify digitally acquired objects. The system is able to recognize things, people or animals and, at the same time, learn from situations and store information in the digital memory.
The concept of deep learning
We have devoted an entire article to deep learning, an artificial intelligence technology destined to change the world. It is a fascinating technology, since its structure is based on that of the human brain, in particular on the interconnection of the various neurons, where it can also process unstructured data.
Aspects of objects cannot always be categorized in advance, so the use of deep learning is critical in particularly complex and articulated tasks. With deep learning, the system is able to identify distinctive characteristics autonomously, without external categorizations. In practice, the system, without any human intervention, is able to verify when the classifications change in response to new input and if it is necessary to introduce new classifications.
We can summarize the differences as follows:
- data format: machine learning uses structured data; deep learning uses unstructured data;
- training: machine learning requires a human trainer; deep learning is based on a self-learning system;
- database: machine learning has a controllable database; deep learning needs over 100 million data points;
- algorithm: machine learning has a variable algorithm; deep learning is based on a neural network of algorithms;
- scope: machine learning is used in routine operations; deep learning finds application in complex tasks.
Differences in the fields of application machine learning vs deep learning
Choosing which of the two technologies to use is of fundamental importance, both to establish the funds to be allocated for the growth of the company and to adopt the right decision-making strategies. Humable has mastered both technologies, allowing our customers to increase productivity by 21% and reduce costs by 15% and waiting times by 19%.
What are the fields of application for machine learning?
machine learning is particularly effective in the following areas:
- customer assistance, especially as a result of chatbots, which simulate human behavior and learn the appropriate answers to customer questions over time;
- online marketing, specifically collecting valuable data following sectorial market analyzes;
- sales, particularly anticipating the potential needs of customers, to provide them with the desired services and products;
- intelligent business, so as to analyze company data and provide useful information to make forecasts and adopt future strategies.
What are the fields of application for deep learning?
deep learning is instead applied in these fields:
- customer assistance, always with the use of chatbots, which are even more advanced in terms of performance and efficiency;
- content creation, or automating the creation of content;
- IT security, as it is able to perceive and identify all threats, even new and unknown ones;
- voice assistants who interact with humans in private, but also within corporate contexts.
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