What is Deep Learning?

Deep learning is a subset of machine learning that uses neural networks, massive amounts of computing power, and huge data sets to create systems that can learn independently. It can perform more complex actions than traditional machine learning models.

Understanding deep learning

To understand deep learning and how it differs from machine learning, you need to understand neural networks.

neural network layer

A neural network (more precisely, an artificial neural network) is a computing system that consists of multiple layers or nodes. Layers are small systems dedicated to specific types of tasks. Combining these layers, the resulting system can learn how to tackle complex multidimensional tasks in a way that simulates the human brain.

Deep learning systems have at least three layers, but usually have more (often 100 or more). In this case, “deep” refers to the multiple layers of these systems, contrasted with less complex machine learning tools.

A deep learning system has at least three layers: input, processing, and output. Data is fed into the system at the input layer, the processing layer performs the intended function of the system, and the output layer provides results or actions. With more layers of deep learning technology, the system becomes more powerful and able to perform complex tasks.

system needs

Running a system with so many layers requires significant computing power. It also requires huge datasets to train deep learning system tasks. Like some machine learning models, deep learning is trained using labeled structured data.

After initial training, deep learning systems tend to require less human intervention than ML models. The longer it runs, the better a deep learning system can perform in analyzing data, detecting patterns, making predictions, and taking action.

How deep learning works

Deep learning is an artificial intelligence application in which multiple layers of neural networks are combined into a single powerful system. We assume that artificial intelligence is the broadest category of this kind of computing. In this case, machine learning is a sub-area of ​​artificial intelligence and deep learning is a sub-area of ​​machine learning. As a result, these systems are often useful for enhancing AI.

Deep learning techniques can perform more functions than machine learning. Also, its features are still being developed and improved, so it requires a lot of computer processing power. Still, the materials needed to make this happen, such as relatively affordable and very powerful computers and huge data sets, have only become available in recent years, and their use has increased.

Comparing Deep Learning and Machine Learning

The two main differences between deep learning and machine learning are how features are defined and actions are taken.

ML models can be trained to detect patterns and recognize objects. Still, doing that requires a human programmer to define the characteristics of those objects (if the model is intended to detect stop signs, the programmer defines the characteristics of the model’s stop signs). need to do it). Deep learning systems, on the other hand, can capture features of stop signs without human input and apply those features to analytical tasks.

The ability to take action is another differentiator between deep learning and machine learning. ML models are often best suited for analyzing data, making predictions, and performing tasks of limited complexity and risk, such as filtering spam and recommending content.

Deep learning can do all these things, but it can also perform the task of performing complex actions at a very fast pace without human supervision. Automated stock trading systems used in the financial industry are a good example.

ML models can analyze historical stock performance and make recommendations to stockbrokers. Instead of analyzing performance and making recommendations, deep learning models can automatically buy and sell stocks many times faster without human intervention based on that logic.

Common use

Deep learning consists of multiple specialized and powerful layers, allowing it to perform highly complex tasks. Used in industries such as:

• Financial business: It is used for automated algorithmic stock trading, near-instantaneous decisions on credit applications, and to detect and respond to potential fraud.
• health care: It is used in some medical studies and for reviewing medical images for test results to make diagnoses more accurately and quickly than humans.
• Voice interface: This technique excels at handling situations of ambiguity and lack of clarity, making it valuable for voice interface systems. Because spoken language is indirect and uncertain of meaning and intent (think of how the same word can mean different things in different contexts), deep learning capabilities that learn incrementally include Siri, Google Now, language It helps provide valuable results in spoken language interfaces such as translation. tool.
• Self-driving car: Self-driving cars present some of the most complex challenges in computing. These systems can help meet these challenges by being able to perform many complex calculations almost instantaneously. For example, a self-driving car can calculate distances to other cars and pedestrians and predict how other vehicles and objects will behave (does that person get off the curb? bounce?), you need the ability to adjust. Fly.