AI has moved far beyond what humans could imagine a decade ago. The outputs that ML models produce are often times a black box's work for a normal human being and even for Data scientists sometimes. Though AI technology is achieving better and more significant goals than humans can in many fields, the results for image processing still do not match human abilities.
The human brain's workings are very complex. Its cognition and rendering mechanisms are still a mystery. A human brain consists of many layers of interconnected neurons, and AI is trying to mimic this structure with artificial neurons to achieve better or at least similar results to the brain. In the mid-20s, scientists developed the concept of artificial neural networks, which can learn from data in a theoretical form. It took more than three decades to see a real example: the AI system AlexNet for computer vision.
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Convolutional Neural Network (CNN) is an artificial neural network with multiple input and output layers, mainly used for computer vision.
According to Wikipedia In mathematics, convolution is a mathematical operation on two functions that produces a third function that expresses how the shape of one is modified by the other. The term convolution refers to both the result function and the computing process. This concept breaks the image into multiple parts and analyzes them independently.
CNN is a layers-based system, and the different layers have different mathematical compositions. The main types of layers are convolution, pooling, and fully connected layers.
In this layer system, one layer's output is input for the other layer, and the complexity increases layer by layer.
The various types of CNN Architectures are listed below:
LeNet is among the first successful CNN projects. This method is also
recommended to beginners as the "Hello World" code. In 1998 the first use case for this deep learning technique was used to recognize handwritten digits.
This model includes five convolution layers and two fully connected layers. Due to the vanishing gradients, the training was not easy for this model, which was later compensated with "Max-pooling" as a connection layer between convolutional layers. This made the training easy by preventing overfitting.
The concept of "Max-pooling" was accepted to the extent that this new AlexNet network combines 5 max-pooling layers,3 fully connected and two dropouts.
Though this architecture is quite similar to LeNet, but much deeper stacked layers. This could accumulate around 60 million features.
The ZF CNN architecture uses other layers between CNN, known as deconvolutional layers, which makes it more efficient than AlexNet.
GoogLeNet is the architecture used by Google in the 2014 event of ILSVRC.
It has models with a reduced error rate in comparison with previous winners. Street view house number detection was the most recognized use case.
VGGNet can work on 4096 convolutional features, with 16-layers CNN with up to 95 million parameters, which can be trained on over one billion images.
This is too expensive to train and needs huge data.
ResNet architecture is the most profound network with 152 layers, which can take more months to train, and 32 GPU power. ResNet used CNN successfully to solve natural language processing problems like sentence building or machine comprehension.
Microsoft's machine comprehension system is one of the use cases of ResNet. These networks can be scaled up or down, considering the computational power of GPUs.
MobileNets has made CNNs possible for a mobile device for image processing and low latency.
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Implementing a convolutional neural network (ConvNet) consists of several steps.
It is important to note that implementation details such as exact architectures, optimization algorithms, and loss functions can vary greatly depending on the type of task and input data.
Convolutional Neural Networks (CNNs) are widely used in various applications such as:
A division of unsupervised learning which makes it more handful because it can also handle unsupervised learning which is itself a big plus. Click to explore here about Generative Adversarial Networks Applications
Businesses such as Facebook, Google, Pinterest, Instagram, and others have started using CNNs to help enterprises in growth. As a result, five major applications have been discovered that people encounter daily:
One of the main applications of this architecture is facial recognition. Using this technique, facial images are broken down into multiple components. The significant components are separating facial features from external features like light or pose and unique facial features.
The documents, including handwritten materials, can be analyzed using CNN architectures. The error rate of comparison of documents with available content is reduced to near zero. Thousands of simultaneous commands run to analyze the handwritten content using CNN, which is very difficult otherwise.
Besides Image processing, neuron networks are also useful for recognizing speech with a huge range of vocabulary and phonics. Emotional detection using CNN is also a focus area for researchers.
Video events like fire or other unusual events can be detected using CNN characteristics. The spatial and temporal information present in videos are the main features when working with Video analysis.
The feedforward neural network is the first and most straightforward artificial neural network. In this, the information moves in only one direction, forward from the input nodes, through the hidden nodes, and to the output nodes. There are no cycles in the network.
Convolutional neural networks (CNNs) can detect heart disease by analyzing medical images such as electrocardiogram signals, MRI, and CT scans. You can train a CNN on a large dataset of images to learn the features of healthy and unhealthy heart images. During inference, the model can classify new images as healthy or indicate the presence of heart disease. This helps in the early detection of heart disease, which is essential for effective treatment. However, it is important to note that CNNs should not be used as the sole tool for detecting heart disease, and a physician should always confirm results.
Convolutional neural networks (CNNs) can predict customer lifetime value (CLV) in the video game industry. CLV measures the value a customer brings to a company over their lifetime. In the video game industry, CLV can be influenced by player behavior, consumer behavior, and game interaction.
CNNs can be trained using historical player data such as in-game behavior, consumption patterns, and demographics to learn patterns that indicate players with high CLV. The model can predict a new player's CLV during inference, allowing gaming companies to prioritize efforts to retain and monetize quality players.
It's important to note that CLV forecasting is a complex undertaking and requires a thorough understanding of the video game industry, player behavior, and spending patterns. CNN should be used with other statistical techniques and industry knowledge to make informed decisions about player monetization and retention strategies. Ethical Concerns Related to convolutional neural networks
Neural networks have greatly achieved human intentions to bridge the gap between artificial intelligence systems and human capabilities. Their applications have increased, from image processing to climate change detection. The architecture has also improved since its first version, LeNet 1998. With improvements, new use cases arise, reducing errors and providing more accurate results.
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