Deep learning is gaining increased interest in several industries. This field forms part of machine learning. The idea behind deep learning is to take cues in the way the brain functions. The structure also mimics that of the brain. The resulting system is an artificial neural network.

Deep learning has numerous branches, usually measured by the dimensions of the field. While Euclidean data comprises of representations in 1D and 2D depths, geometric deep learning is based on the principle of 3D. We take a look at what geometric deep learning is and how it will play a role in the future.

In the past, a majority of research and development with deep learning was related to the first and second dimensions. The Euclidean methods that have been used place certain limitations on the opportunity for expanding deep learning. This is because the real world has a third dimension not targeted by these models of deep learning.

Geometric deep learning focuses on surpassing the current methods in deep learning, primarily by focusing on including this third dimension in the artificial neural networks created through the technology.

By focusing on a 3D model instead of the standard 1D and 2D dimensional approaches, it is possible to reach closer to human-level networks, as this is the dimension that manifests itself in the real world.

Euclidean data models have been focusing on subjects the following subjects throughout the past years:

Computer vision

Speech recognition

Language transition

Image generation

While Euclidean data has already provided successful results in the past, the limitations faced have halted progress in certain areas.

Complex data is processed in areas like biology and physics, as well as in network science. By using Euclidean data, complex data cannot be processed efficiently – due to the simple dimensions used in the process.

By turning to geometric deep learning, these limitations can be effectively overcome. Researchers have found that non-Euclidean data is able to process data that is complex faster and more efficiently while also delivering results that are closer to a human level.

There are several examples of how non-Euclidean data types are utilized in the modern world. Among all data types, researchers and scientists generally turn to graphs most frequently.

A social media platform can be represented by a graph. The graph consists of nodes. A social platform like Facebook would have millions of nodes – as each node represents a user on the network. Graphs in non-Euclidean models consist of more than just the nodes, however. Edges are used as connectors between different nodes in a network. With a platform like Facebook, the edges would represent actions performed by users.

In this example, two people would form nodes – and a conversation that occurs between the two users creates an edge that runs between the two nodes.

Geometric deep learning can also include the use of manifolds as a data type. In this data model, a multi-dimensional system is used – which is where the 3D environment of geometric deep learning comes into play. The multi-dimensional space seen in a manifold data type is represented by a shape with three dimensions. The space would have a vast number of points that help in the creation of the shape.

As geometric deep learning advances, we are seeing this technology implemented in various industries. A good example would be the pharmaceutical industry, where geometric deep learning has the potential to assist with the process of drug discovery.

With drug discovery, a three-dimensional model of graph data types is used. Molecules that are in existence and previously discovered are modeled into the graph. Nodes in the graph represent the atoms that can be used in the development of molecules, while the edges in the graph represent the bonds between atoms.

Through geometric deep learning, technology can compare millions of atoms and molecules in order to find new drug options to treat existing diseases. This may be especially helpful in cases where chronic diseases are difficult to treat. New combinations of molecules and atoms can be identified with the help of geometric deep learning. These discoveries can then be analyzed by scientists, allowing them to determine if the molecules would have the potential to help patients with the disease.

There are a few other examples of non-Euclidean spaces and data types that can be used in real-world scenarios. Social sciences, for example, would create a 3D model of social networks to gain a more advanced understanding of human behavior. This would take the basic graph model of social networks to the next level.

Moving from two and three-dimensional spaces is important for the future of deep learning. Geometric deep learning is a field that is gaining more interest due to a non-Euclidean data type used in this model. The addition of a third dimension aims to deliver advancement in the machine and deep learning strategies that would allow the technology to achieve efficiency closer to the human brain.

Introduction: What Is Geometric Deep Learning?Deep learning is gaining increased interest in several industries. This field forms part of machine learning. The idea behind deep learning is to take cues in the way the brain functions. The structure also mimics that of the brain. The resulting system is an artificial neural network.

Deep learning has numerous branches, usually measured by the dimensions of the field. While Euclidean data comprises of representations in 1D and 2D depths, geometric deep learning is based on the principle of 3D. We take a look at what geometric deep learning is and how it will play a role in the future.

Also Read: AI in Drug DiscoverySurpassing Deep Learning MethodsIn the past, a majority of research and development with deep learning was related to the first and second dimensions. The Euclidean methods that have been used place certain limitations on the opportunity for expanding deep learning. This is because the real world has a third dimension not targeted by these models of deep learning.

Geometric deep learning focuses on surpassing the current methods in deep learning, primarily by focusing on including this third dimension in the artificial neural networks created through the technology.

By focusing on a 3D model instead of the standard 1D and 2D dimensional approaches, it is possible to reach closer to human-level networks, as this is the dimension that manifests itself in the real world.

Euclidean data models have been focusing on subjects the following subjects throughout the past years:

While Euclidean data has already provided successful results in the past, the limitations faced have halted progress in certain areas.

Complex data is processed in areas like biology and physics, as well as in network science. By using Euclidean data, complex data cannot be processed efficiently – due to the simple dimensions used in the process.

Source: YouTube | Geometric deep learning.

By turning to geometric deep learning, these limitations can be effectively overcome. Researchers have found that non-Euclidean data is able to process data that is complex faster and more efficiently while also delivering results that are closer to a human level.

Also Read: AI In Robotics: an Assimilation For The Next Phase In Technology.Non-Euclidean Data TypesThere are several examples of how non-Euclidean data types are utilized in the modern world. Among all data types, researchers and scientists generally turn to graphs most frequently.

A social media platform can be represented by a graph. The graph consists of nodes. A social platform like Facebook would have millions of nodes – as each node represents a user on the network. Graphs in non-Euclidean models consist of more than just the nodes, however. Edges are used as connectors between different nodes in a network. With a platform like Facebook, the edges would represent actions performed by users.

In this example, two people would form nodes – and a conversation that occurs between the two users creates an edge that runs between the two nodes.

Geometric deep learning can also include the use of manifolds as a data type. In this data model, a multi-dimensional system is used – which is where the 3D environment of geometric deep learning comes into play. The multi-dimensional space seen in a manifold data type is represented by a shape with three dimensions. The space would have a vast number of points that help in the creation of the shape.

As geometric deep learning advances, we are seeing this technology implemented in various industries. A good example would be the pharmaceutical industry, where geometric deep learning has the potential to assist with the process of drug discovery.

With drug discovery, a three-dimensional model of graph data types is used. Molecules that are in existence and previously discovered are modeled into the graph. Nodes in the graph represent the atoms that can be used in the development of molecules, while the edges in the graph represent the bonds between atoms.

Through geometric deep learning, technology can compare millions of atoms and molecules in order to find new drug options to treat existing diseases. This may be especially helpful in cases where chronic diseases are difficult to treat. New combinations of molecules and atoms can be identified with the help of geometric deep learning. These discoveries can then be analyzed by scientists, allowing them to determine if the molecules would have the potential to help patients with the disease.

There are a few other examples of non-Euclidean spaces and data types that can be used in real-world scenarios. Social sciences, for example, would create a 3D model of social networks to gain a more advanced understanding of human behavior. This would take the basic graph model of social networks to the next level.

Also Read: The Role of Artificial Intelligence in Healthcare Documentation.Conclusion:What Is Geometric Deep Learning?Moving from two and three-dimensional spaces is important for the future of deep learning. Geometric deep learning is a field that is gaining more interest due to a non-Euclidean data type used in this model. The addition of a third dimension aims to deliver advancement in the machine and deep learning strategies that would allow the technology to achieve efficiency closer to the human brain.

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