Today, data analysis is a key factor in business decision-making. This data needs to be pre-processed and analyzed using Artificial Intelligence (AI) and Machine Learning methods such as Deep Learning. AI has thus become a reality with multiple everyday applications, the number of which will only increase in the years to come. To help you better understand these key concepts, we thought it would be a good idea to define them a little more clearly.
For several years now, Artificial Intelligence, Machine Learning and Deep Learning have been used for a huge variety of applications. So much so that these terms are often mistakenly confused.
Although they are related, each of these concepts has its own meaning. In simple terms, Deep Learning is a sub-category of Machine Learning, itself a sub-category of Artificial Intelligence. In this dossier, find out what each of these terms means, and how they differ from one another!
What is artificial intelligence?
Artificial Intelligence is the science of enabling machines to think and act like humans. It aims to endow a computer with intelligence comparable to that of a human being.
However, no computer processor currently exists that can rival the human brain. Although machines excel at applying rules and carrying out tasks, a simple action for a human can be extremely complex for a computer.
For example, carrying a tray of drinks into a bar and serving each glass to the right customer is a waiter’s job that any human can perform, although some will have more skill.
Yet it’s a complex decision-making exercise based on a vast volume of data transmitted between neurons and the human brain.
Computers are not yet capable of doing this job as effectively as a human. However, Machine Learning and Deep Learning are a big step in this direction, enabling them to analyze large volumes of data and make decisions based on them without human intervention…
What is Machine Learning?
Machine Learning was defined by its pioneer Arthur Samuel in 1959 as “the field of study which gives computers the ability to learn without being explicitly programmed to learn”.
In effect, it’s an approach based on statistical analysis, enabling computers to improve their performance on the basis of data, and to solve tasks without being explicitly programmed to do so.
Depending on the presence or absence of targets, learning can be classified into several types: supervised, semi-supervised, unsupervised or reinforcement learning.
Machine Learning is a very active field of research, with new algorithms and fields of application being discovered every day. There is therefore a growing need for automatic algorithms to analyze and make sense of this data, to make predictions, or to gain a better understanding of the process that generates this data.
What is Deep Learning?
Deep Learning, a sub-category of Machine Learning, is an automatic learning method inspired by the nervous system of living beings.
Deep Learning algorithms process incoming information in a similar way to the way our neural networks respond to nerve signals.
Depending on the type and frequency of messages received, some neural networks will expand quantitatively and qualitatively, while others will regress.
Deep Learning is making great strides in solving problems that have resisted the best attempts of the artificial intelligence community for many years.
It has proved very good at discovering complex structures in high-dimensional data and is therefore applicable to many areas of science, business and government.
However, most of the time, it requires a large volume of data and therefore a lot of processing power to build and operate a neural network.
Deep Learning vs. Machine Learning: what are the differences?
Machine Learning and Deep Learning are two types of artificial intelligence. Machine Learning is AI capable of automatic adaptation with minimal human interference, and Deep Learning is a subset of Machine Learning using neural networks to mimic the learning process of the human brain.
Several major differences separate these two concepts. Deep Learning requires larger volumes of training data, but learns from its own environment and mistakes.
In contrast, Machine Learning enables training on smaller datasets, but requires more human intervention to learn and correct mistakes.
In the case of Machine Learning, a human must intervene to label the data and indicate its characteristics. A Deep Learning system, on the other hand, attempts to learn these characteristics without human intervention.
For facial recognition, for example, the Deep Learning program first learns to detect and recognize facial borders and lines. It then learns the most important parts of faces, and finally the general representation of faces. This requires huge volumes of data, but the probability of success increases as training progresses.
The approach is radically different. Machine Learning algorithms tend to separate data into several parts, which are then combined to propose a result or solution. Deep Learning systems, on the other hand, consider a problem in its entirety.
Machine Learning requires less training time, but its level of precision is lower. Deep Learning enables the machine to make complex, non-linear correlations between data.
Deep Learning training takes much longer, due to the large amount of data to be processed, and the many parameters and mathematical formulas involved. A Machine Learning system can be trained in seconds or hours, whereas Deep Learning can take weeks.
Finally, Machine Learning can be trained on a CPU (central processing unit), whereas Deep Learning requires a GPU (graphics processing unit). This powerful hardware is indispensable for processing large volumes of data and performing complex algorithm calculations.
Given their differences, Machine Learning and Deep Learning are used for different applications. Machine Learning is exploited by predictive programs for finance or weather, email spam identifiers, or programs to design personalized treatments for patients.
Deep Learning is used for streaming service recommendations, facial recognition, but also for autonomous vehicles. Thanks to neural networks, cars are able to determine which objects to avoid, recognize traffic lights and signs, and know when to speed up or slow down.
Machine Learning and Deep Learning: A six-point summary
To navigate serenely between Machine Learning and Deep Learning, discover this summary table specially designed by DataScientest for you:
| Aspect | Machine Learning | Deep Learning |
|---|---|---|
| Data Dependency | Typically requires structured data. | Well-suited for unstructured data, e.g., images, audio, text. |
| Feature Engineering | Requires manual feature engineering. | Automatically learns features from data. |
| Network Depth | Shallower network architectures. | Involves deep neural networks with multiple hidden layers. |
| Training Data Size | Works well with smaller datasets. | Benefits from large datasets for better performance. |
| Interpretability | Easier to interpret and explain results. | Complex models can be less interpretable. |
| Computation Resources | Requires fewer computational resources. | Demands significant computational power (GPUs/TPUs). |
| Use Cases | Common for tasks like regression, classification. | Ideal for tasks like image recognition, NLP, and more complex problems. |
| Training Time | Faster training with smaller models. | Longer training due to deeper architectures. |
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