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Everything you need to know about geometric deep learning and the various networks involved in it
Recurrent neural networks (RNN) and convolutional neural networks (CNN) are two examples of deep learning algorithms that have made substantial progress in recent years in solving problems in a variety of domains, including speech recognition, computer vision, and many others. The results were extremely accurate, however, it mostly worked with Euclidean data. However, we have to deal with non-euclidean data, such as manifolds and graphs, when it comes to Network Science, Physics, Biology, Computer Graphics, and Recommender Systems. Geometric Deep Learning applies deep learning techniques as a whole to the manifold or graph-structured data to deal with this non-euclidean data.
As more and more academics use 3D data to create AI models, working with 2D data is becoming increasingly obsolete. The field, known as geometric deep learning, works with difficult data, like graphs, to produce effective models. Michael M. Bronstein first brought up geometric deep learning in the paper Geometric deep learning: extending beyond Euclidean data, and it is now being used in fields including 3D object classification, graph analytics, 3D object correspondence, and more.
Neuroevolution
According to Floreano et al. (2008), evolutionary methods should only be used to improve the architecture of neural networks, as gradient-based methods perform better for optimizing neural network weights. In addition to selecting the appropriate genetic evolution parameters, such as the mutation rate, mortality rate, etc. Additionally, it is important to assess the precise manner in which the genotypes employed for digital evolution represent the topologies of neural networks.
Reinforcement Learning
The hunt for improved architectures has been effectively driven by the application of reinforcement learning. The primary bottleneck in a NAS algorithm is often the capacity to efficiently navigate the search space to conserve valuable computational and memory resources. A high validation accuracy is frequently achieved at the expense of model complexity, which results in a larger number of parameters, a need for additional memory, and longer inference times.
Artificial Neural Network
It is a particular class of feed-forward neural networks. Without reviewing the previous layers, information moves from one layer to the next. It is intended to spot patterns in unprocessed data and get better with each new input. Three levels make up the design architecture, and each layer strengthens the flow of information. Because they can learn non-linear functions, they are also referred to as "universal functional approximators." It has a few disadvantages and benefits over other algorithms and is primarily employed in predictive processes like business intelligence, text prediction, spam email identification, etc.
Designing the Search Strategy
Finding out which optimization techniques are most effective and how to modify or alter them so that the search process produces better results more quickly and steadily has accounted for the majority of the work that has gone into neural architecture search. The use of Bayesian optimization, reinforcement learning, neuroevolution, network morphing, and game theory are just a few of the methods that have been tried.
Convolution Neural Network
It has three layers: a convolutional layer, a pooling layer, and a fully-connected layer, and is widely used for computer vision applications. CNN networks use computer vision, which is used in picture identification. With each layer, algorithms become more complex. They run the input through several kernel-style filters to analyze it. To extract features from the photos, they act as matrices that move across the input data. The connections between neurons emerge as kernels in the layers when the incoming images are analyzed. For instance, when processing a picture, kernels move through successive layers and alter as necessary to recognize colors, shapes, and ultimately the entire image.
Recurrent Neural Networks
The two cornerstones of the RNN network are natural language processing and voice recognition. RNN algorithms make it feasible for services like Picasa's face detection technology, Google Translate, and voice search using Apple's Siri. Feed-forward networks do not use memory, although RNN networks do. While inputs and outputs for conventional neural networks are thought to be independent, the RNN network relies on earlier outputs in the sequence. RTT Networks employ a backpropagation method that is unique to the entire sequence of data and slightly different from that utilized by other networks.