In the context of Machine Learning (ML) and Artificial Intelligence (AI), the relevance of neural networks is indispensable. The role of neural networks is similar to that of human nervous systems. The origin of neural networks has led to the exponential growth of the capabilities of modern technologies. If you are keen on making a career in machine learning, it is a necessity for you to broaden your knowledge of neural networks.
By having a clear understanding of neural networks, you can understand how they are utilized on the basis of their unique specifications and mathematical operations. Are you wondering which is the best neural network? If so, you will get an answer to the question. But before that, you need to have a clear understanding of what neural networks are.
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What are Neural Networks?
A neural network is basically a machine learning model that is capable of making decisions similar to the human brain. What makes it unique is the fact that it involves processes that imitate the manner in which biological neurons function collaboratively in the human brain to recognize an event, weigh the available options, and arrive at conclusions. Neural networks depend on training data for the purpose of learning and enhancing their level of accuracy.
As the relevance of neural networks is high in the ML and AI domain, a broad range of neural networks have come into existence. If you want to make a career in the artificial intelligence or machine learning domain, you need to have a solid foundation in neural networks. If you plan to learn about neural networks, you need to be aware of the most popular neural networks. The insight can definitely boost your career prospects in the future.
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List of Top Neural Networks
There are a number of neural networks that are currently transforming the capabilities of artificial intelligence, machine learning, and deep learning. If you want to be a step ahead of your competitors in the modern technology domain, you certainly need to know about the top neural networks.
However, you do not have to feel overwhelmed about it. Instead, the valuable insight can surely help you identify how you need to learn about the concept of neural networks. The neural networks list captures the top neural networks that you need to look out for currently.
1. LeNet5
LeNet5 is one of the most popular neural network architectures that you will come across today. It can be rightly said that the neural network brought a revolutionary change in the deep learning domain. The architecture of LeNet5 is quite fundamental. Although it came into existence in 1994, it continues to be of immense relevance.
One of the key benefits of the neural network architecture is the ability to save in relation to computation. LeNet5 is surely among the best neural networks that have ever come into existence. The popularity of LeNet5 is evident from the fact that it has led the way for several new neural networks.
2. AlexNet
While discussing the topic of neural networks, it is impossible to ignore AlexNet. It is considered to be among the top neural networks that you can find currently. It is a much more thorough and broader version of LeNet. If you decide to learn a particular neural network, you can learn about complex objects and hierarchies. As the capabilities of machine learning and deep learning expand with each passing day, you need to have a solid grip on AlexNet. The knowledge in this area can definitely give you an edge over your competitors and rivals.
3. Network-in-Network
The network-in-network is an immensely popular neural network architecture. One of its core features is its ability to provide higher combination power. That’s not all! The fact that network-in-network is simple and offers valuable insight adds to its uniqueness. Learning about a particular neural network can be of great help to individuals who want to grow professionally in the domain. Its fundamental idea is to apply a totally connected layer at every pixel location. Although you may feel overwhelmed initially, after getting a hold of the concept, you will surely enjoy the learning process.
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4. VGG
The VGG neural network stands for visual geometry group. The architecture of VGG serves as the foundation for ground-breaking object recognition models. It is surely among the most popular and widely used image recognition architectures available today. VGG comes with multiple layers that influence its capabilities as a neural network. The VGG model, which is capable of supporting 16 layers, is known as VGG16. Similarly, the VGG with a total of 19 convolutional layers is called VGG19.
5. ResNet
Resnet is certainly one of the most straightforward neural networks that you may come across today. Its basic idea is to bypass the input to the succeeding or next layer. Moreover, it also focuses on feeding the output to two consecutive convolutional layers. The neural network can stack numerous identity mappings and make use of the activations of the prior layers. Hence, it helps speed up the initial training process.
6. GoogLeNet or Inception
The computation processes relating to deep neural networks may be burdensome at times. GoogLeNet is the inception architecture whose intention is to reduce the burden of such computations. The neural network is quite different from other architectures since it involves a diverse type of methods. Some of the common methods are 1 x 1 convolution and global average pooling. These elements boost its capabilities and increase the depth of the neural network.
7. Bottleneck Layer
The Bottleneck Layer encompasses a smaller number of nodes than the previous layers. It is capable of converting the input data into a compressed or compact form of representation. Due to such a feature, having the layer is beneficial. It can support the best possible fit within the available space. In the inception network of Google, these layers are included to minimize the total number of feature maps. It is of immense value since it helps in minimizing the number of parameters that exist in a network.
8. Overfeat
Overfeat came into existence in the year 2013. It is a fairly new derivative of the AlexNet neural network. It is a classic variety of convolution neural network since it deploys convolution. Pooling along with totally connected layers. It is definitely among the most popular neural networks that focus on classification, localization, and detection. Initially, Overfeat focuses on training an image classifier. Then, it progresses to fixing the feature layer. Ultimately, it trains the boundary regressor. Learning about the neural network can surely help you move ahead in the deep learning arena while broadening your capabilities.
9. SqueezeNet
When SqueezeNet came into existence, the goal of the team was to create a tiny neural network that had fewer parameters. However, the reduction in parameters did not affect the competitive accuracy in any manner. The neural network came into existence when the concepts of ResNet and Inception were rehashed. The architecture design is light and efficient due to the elimination of complex compression algorithms.
10. ENet
One cannot miss out on the ENet neural network, which is known for being efficient and lightweight in nature. Since the neural network makes use of a few parameters and computations, it is highly robust. All the contemporary architectural features are present in ENet, which enhances its functionality and usefulness. It allows for carrying out real-time pixel-wise semantic segmentation. This feature makes it highly relevant in mobile applications.
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Rapid evolution in neural networks
The neural network landscape is undergoing dynamic change. There is no denying the importance of neural networks in the deep learning and artificial intelligence landscape. Due to continuous efforts, new types of neural networks are emerging that have better capabilities than already existing neural networks. If you want to thrive as a professional, you must have high proficiency in neural networks and how they function. The insight can surely help you go a long way in your career.
In the competitive era, you need to keep up with the times and master your skills and knowledge about neural networks. Some of the most popular neural networks that exist today have been listed for you. If you plan on learning about neural networks, the list can surely be of immense help to you. However, you need to go a step ahead and gain comprehensive insights into the top neural networks so that you can put the knowledge to practical use.
Final Words
The emergence of the neural network concept has been revolutionary in the domain of deep learning. It has shown the ability of machines to mimic the decision-making ability of human beings. The list capturing the top neural networks that are available today has been presented to you. By learning about the best neural networks, you can expand your knowledge of the area and put your skills to optimum use. If you are keen on going up the professional ladder in the IT domain, it is time to have a solid grasp of neural networks.
About Author
James Mitchell is a seasoned technology writer and industry expert with a passion for exploring the latest advancements in artificial intelligence, machine learning, and emerging technologies. With a knack for simplifying complex concepts, James brings a wealth of knowledge and insight to his articles, helping readers stay informed and inspired in the ever-evolving world of tech.
