Hi readers! I hope you are doing great. We are learning about modern neural networks in deep learning, and in the previous lecture, we saw the capsule neural networks that work with the help of a group of neurons in the form of capsules. Today we will discuss the graph neural network in detail.
Graph neural networks are one of the most basic and trending networks, and a lot of research has been done on them. As a result, there are multiple types of GNNs, and the architecture of these networks is a little bit more complex than the other networks. We will start the discussion with the introduction of GNN.
The work on graphical neural networks started in the 2000s when researchers explored graph-based semi-supervised learning in the neural network. The advancements in the studies led to the invention of new neural networks that specifically deal with graphical information. The structure of GNN is highly influenced by the workings of convolutional neural networks. More research was done on the GNN when the simple CNN was not enough to present optimal results because of the complex structure of the data and its arbitrary size.
All neural networks have a specific pattern to deal with the data input. In graph neural networks, the information is processed in the form of graphs (details are in the next section). These can capture complex dependencies with the help of connected graphs. Let us learn about the graph in the neural network to understand its architecture.
A graph is a powerful representation of data in the form of a connected network of entities. It is a data structure that represents the complex relationship and interaction between the data. It consists of two parts:
Node
Edge
Let us understand both of these in detail.
Here, nodes are also known as vertices, and these are the entities or data points in the graph. Simple examples of nodes are people, places, and things. These are associated with the features or attributes that describe the node, and these are known as node features. These features vary according to the type of graphical network. For instance, in the social network, the node is the user profile and the node features include its age, nation, gender, interests, etc.
Edges are the connections between the nodes, and these are also known as the connections, links, or relationships of the nodes. Edges may be directional or unidirectional in nature, and these play a vital role in the connection of one node to the other. The unidirectional nodes represent the relationship of one node to the other, and the undirected edges represent the bidirectional relationship between the nodes.
Just like other neural networks, the GNN also relies on multiple layers. In GNN, each layer is responsible for getting information from the neighbor node. It follows the message-passing paradigm for the flow of information; therefore, the GNN consists of inherited relationships and interactions among the graphs. In addition to nodes and edges, here are some key features to understand the architecture of GNN.
The complex architecture of layers in the graph is responsible for the flow of information from node to node. The message-passing process is part of the information flow when every node interacts with each other to provide information, and as a result, the data is transformed into an informative message. The type of node is responsible for the particular information, and nodes are connected according to their node features.
mechanisms. The aggregation of the data is done through a weighted sum or even more complex mechanisms such as mean aggregation or attention-based aggregation.
The GNN follows the learnable parameters just like some other neural networks. These are the weights and biases that are learned during the processes in the GNN. The state of each node is updated based on these parameters. In GNN, the learnable parameters have two properties:
Since its introduction in the 2000s, continuous research and work have been done on the GNN. With the advent of research, there are multiple types of GNNs that are working in the market for particular tasks. Here are some important types of graphical neural networks:
The graph convolutional networks (GCN) are inspired by convolutional neural networks. These are the earliest and most widely used GNN variants. These networks can learn the data by applying the convolutions to the graph data. In this way, these can aggregate and update node representation by keeping track of their neighbor nodes.
These are inspired by recurrent neural networks and are also referred to as GRN. The basic use of these networks is in sequence modeling. These networks apply the recurrent operations to the graph data and learn features from it. These features are representative of the global structure.
The graph attention networks (GATs) introduce the attention mechanism in the GNNs. This mechanism is used to learn the weights of edges in the graph. This helps in the message passing because the nodes choose the relevant neighbors and it makes the overall working of the network easy. The GATs work perfectly in processes like node classifications and recommendations.
The graph isomorphism network was introduced in 2018, and it can produce the same output as the two isomorphic graphs. GINs focus on the structural information of graphs and apply premature invariant functions during the steps of message passing and node update. Each node represents its data, and the most likely connected nodes are aggregated to create a more powerful network.
GraphSAGE means graph sample and aggregated, which is a popular GNN architecture. It samples the local neighborhood of each node and aggregates its features. In this way, the detail of node data is easily represented, and as a result, scalability can be applied to large graphs. It makes graph learning tasks easy, such as the classification of nodes and link prediction.
The large collection of types of GNN architecture allows it to perform multiple tasks. Here are some important applications of GNN in various domains:
GNN has applications in social networks, where it can model relationships among network entities. As a result, it performs tasks such as link prediction, recommendation analysis, community detection, etc.
The GNN plays an informative role in the medical industry in branches like bioinformatics and drug discovery. It is used in the prediction of the molecular properties of new drugs, the protein-protein interaction in the body and drugs, the formulation of new drugs based on experimentation, etc.
The relationship between the graphs is a string in the GNNs, which makes it ideal for prediction and learning the interaction between the user and the items. Moreover, the graph structures are highly usable in the recommendation system of the items released for the users on different levels.
Hence, we have read the information about the graph neural networks. The basic unit of these networks is the graph, which has two parts nodes and edges. The relationship between different edges in the group is responsible for the functioning of GNN. We have seen the types of neural networks that are divided based on their mechanisms of working. In the end, we had an overview of the general applications of GNN. These are a little bit more complex neural networks as compared to other modern networks we have read in this series. In the next lecture, we will discuss another modern neural network.
Hey pupil! Welcome to the next lecture on modern neural networks. I hope you are doing great. In the previous lecture, we saw the EffcientNet neural network, which is a convolutional Neural Network (CNN), and its properties. Today, we are talking about another CNN network called the capsule neural network, or CapsNets. These networks were introduced to provide the capsulation in CNNs to provide better functionalities.
In this article, we will start with the introduction of the capsule neural network. After that, we will compare these with the traditional convolutional neural networks and learn some basic applications of these networks. So, let’s start learning.
Capsule neural networks are a type of artificial neural network that was introduced to overcome the limitations of CNNs. In 2017, these modern neural networks were designed by Geoffrey Hinton and his team working in the Google AI research center.
These are some of the most popular and searched neural networks because they deal with the inefficiency of CNN in recognizing the results when the input data has different orientations. The capsule Neural networks are made by getting inspiration from the visual cortex of the human brain to process information.
The capsule neural network is one of the most prominent deep learning architectures and is widely used in fields like computer vision for processes like image classification, object detection, and segmentation. If you know about convolutional neural networks, then you must know that they are relatively difficult to process and require a great deal of information to work properly. Hence, to make the neural network more powerful, different neural networks, such as capsule neural networks and EffiecnetNet, are introduced.
The neural networks are categorized in different ways on the basis of their arrangement of layers. Usually, the neural networks have the same structure but slightly different performance and other features. However, the workings of CapsNet are far more different from those of traditional neural networks; therefore, there is a need for a detailed study of structure and performance. Here are some key features of Capsule neural networks that make them different from other traditional neural networks:
The name clearly specifies the difference in the workings of this neural network. These are different because the basic building block of Capsnets is the capsule of the neuron. Unlike traditional neural networks, where the neurons are the basic building blocks, CapsNet has a group of neurons (capsule) as the basic building block. Hence, we define the capsule as:
A capsule in the Capsule neural network is the group of neurons that efficiently encodes the features of the images, such as position, orientation, and size.
These features are called the pose of the images and are important in the working of neural networks, especially when networks are specialized for image recognition and related fields.
The most prominent difference to discuss is the structure of the capsule neural network. The capsules are arranged in the form of a hierarchy, where each capsule is responsible for extracting information of a specific type at the given level of abstraction.
The traditional neural networks are arranged in the form of a flat hierarchy, which causes limitations in their working. Capsule neural networks have complex relationships among the features, and therefore, better results can be extracted from the calculations.
A main difference between traditional and capsule neural networks is the dynamic routing mechanism, which is the main power behind the success of this neural network. It is called dynamic routing because it determines the relationship between the adjacent layer and capsule. As a result, the details of the features in the image are effectively determined.
Dynamic routing is helpful in recognizing objects at varying points and angles because capsules reach a consensus on the representation and presence of the properties of the data entity. This is different from traditional neural networks, where the weights are assigned to every neuron, and as a result, these produce the results needed to extract the information.
The way CapsNets recognize the images is up to par because these not only identify the objects but can also identify the poses and angles of the images. In this way, they can recognize the images even if the orientation of the images changes. This is the basic working of the CapsNets.
On the other hand, traditional neural networks require a great deal of data to recognize the images at a certain point, and there are errors when the image alignment is changed. Hence, the CapsNets require less data and provide more efficiency with the help of pose estimation.
This feature helps to regenerate the responses with the novel viewpoint even if the images are deformed. Traditional neural networks are not able to explicitly model pose information. These require extensive data arguments and a large variety of information in the form of datasets.
The CapsNets are arranged in the form of capsules, which enhances the complexity of the network. No doubt, the results obtained are more efficient, but the complexity level of CapsNet is higher than that of traditional neural networks. The capsules are connected in multiple layers, and to deal with them, multiple iterations are required.
Dynamic routing is responsible for the transfer of the output of one capsule to the next capsule in the interconnected layer in a specific pattern. The mechanism of dynamic routing is more expensive.
Another advantage of using CapsNets is its interpretable representation of the results. It can be defined as:
“The interpretable representation of the neural network is its ability to disentangle the representation of the output of the learned features.”
The results of the CapsNets are interpretable and, therefore, more understandable. It provides semantically understandable outputs. As a result, these serve as a bridge between the complex outputs of neural networks and human understandable results.
The pooling layer is a special type of layer in the neural network that is responsible for the reduction of the dimensions of a feature map. It is done with the help of downsampling. In the case of capsule neural networks, there are no pooling layers; instead, the same functionality is accessed with dynamic routing. As a result, the capsule network shows state-of-the-art output for the images.
The part-whole relationship in neural networks is the connection between different parts of the same object. For instance, there is a part-whole connection between the table and the areas where different legs are connected to the flat board to make a table.
It is an important feature in fields like computer vision for processes like object detection, image segmentation, etc. In CapsNet, the part-whole relationship is strong and powerful because these use vectors to encode the pose of objects in an image. On the other hand, traditional CNN uses pooling layers for the same purpose, and it is difficult to get information about part-whole relationships.
Keeping all these differences in mind, we have created a tale for you to quickly review the difference between these layers:
Feature |
Traditional Neural Network |
CapsNets |
Building Block |
Neuron |
Capsule (Group of neurons) |
Layer Connection |
Static |
Dynamic |
Computational complexity |
Less |
More |
Efficacy of data |
Less |
More |
Maturity |
More |
Less |
Hierarchy Type |
Flat |
Interconnected |
Feature Mapping |
Pooling Layer |
Dynamic Routing |
Part-whole Relationship |
Pooling layer |
Vectors |
The capsule neural network has various applications in multiple departments. There is a lot of detail about each of them, but we have set out a simple list of applications for you. Here is it:
In the field of computer vision, there is a great deal of interest in capsule neural networks because they provide the best output in different orientations. The features of CapsNets are helpful in areas like image recognition, face recognition, medical imaging, etc.
Natural language processing requires neural networks that can subdivide the input in different ways. The capsule neural network helps in the processes of document classification and sentimental recognition.
The industry of robotics and its automation require the most efficient ways to teach object recognition to robots. The highly efficient mechanism of the capsule network is perfect for fields like robotics and automation. It helps with object manipulation and visual SLAM in the mentioned industries.
Hence, the capsule neural network is an important type of modern neural network that helps get image-related outputs more efficiently. These are made of capsules of neurons instead of using them singularly. Moreover, the hierarchy and routing systems help the users get the t output. We have seen multiple features of this neural network that are useful and better than the traditional neural network. However, these are more complicated to deal with, but overall, there are multiple applications of capsule neural networks. If you want to know more about modern neural networks then stay with us 9n the next session.
The last decade brought about a lot of advancements that we didn’t think would even be possible. In the case of business communication, the biggest benefit next to the internet is VoIP. Thanks to this technology, all business owners (even those whose budgets are extremely meager) can set up a strong communication system on par with their more established counterparts.
But when talking about how helpful VoIP can be, the conversation is always focused on calls. People always talk about how Telnum and other telecom providers are able to slash their phone bills, enhance the communication features they enjoy, and many more.
What about SMS? Unfortunately, the advantages in this area are unknown to many users. There are so many benefits If you want to fully harness the capabilities that are associated with VoIP, it’s time to expand to using it for SMS as well. Keep reading to find out how it would be advantageous to your enterprise:
Before the internet, mobile phones were pretty convenient because they allow you to receive messages even when you’re on the go. They’re still very useful today if you don’t have an internet connection.
But the problem here is when there’s a problem with the cellular tower or you’re in an area with a weak signal. There’s a chance that you won’t be able to receive your messages. The same issue persists if your phone is turned off. This is problematic for businesses because you want to be as responsive as possible.
By using a virtual number to get your text messages, you’re getting rid of this problem. You’d be able to access the messages anytime, just like you would with an email.
Even though online communication is very developed now, there’s still nothing that can beat a good old text message. From the number, you’re able to see the location of the person.
In a sense, this increases trust. The sense of belongingness that one can feel as being part of the same community is great. The same goes for the peace of mind, knowing that they’re physically accessible should there be issues.
If you’re operating in just one place, these pros may not mean anything to you. But the moment you try to expand to another country to get a wider market, these would matter a lot.
And any expansion may be closer than you think. Today, you don’t have to set up a physical location in a country to try and dominate the market there. Therefore, the costs of expansion are way cheaper. An SMS number (get it here: https://telnum.net/services/sms-numbers/ ) prepares you for this success that can easily come your way.
Get your messages read by your audience
Did you know that the average text message gets read within the first few minutes after it’s received? To compare, most of the emails you send probably don’t get read at all. Some of them may even end up in the spam folder.
So with an SMS number, you can have your messages read too. This allows you to maximize the financial impact of every text you send. Whether there’s an anniversary sale or a new product, you can make sure that your loyal customers always have the latest news.
Another marketing opportunity
No matter the fad in marketing today, the key ingredient would still be getting the message across. Now that you know just how receptive people are to text messages, it’s only right to use it as another opportunity to market to your audience.
You can even integrate it with any existing marketing efforts that you already have in place. With SMS, you can send promotions, increase traffic on your digital assets, and many more. Plus, think about how quick it is to send a text message. Unlike full-on funnels that take weeks to make, you can craft a text message and send it within the same day.
Low risk, high reward
It’s not just the number per se that is cheap. Sending your message to your audience won’t cost you a lot as well. Imagine this: for just a few cents per message, you’re guaranteed to have your message read by the recipient.
That’s one of the best deals that you can find today! This makes it a great marketing venture to try out if you have a small budget. Even if your messaging fails today, you can always try again tomorrow.
So if you haven’t yet, try incorporating an SMS VoIP number into your communication system. With all these benefits and the popular benefit of low fees, there’s no reason for you not to try it now. Get yours today and start experiencing these advantages for yourself!
Technology has moved steadily ahead over the years, but it has evolved by leaps and bounds in the past decade or so. Smartphones have been a revolution and a revelation. Even video games have become increasingly sophisticated and have overtaken the movie industry in value.
If technology keeps developing on this trajectory in the future, the next generation of coders will need online lessons today. Before signing up for your child, here’s what to look for in a program.
For now, put lofty things like your child’s eventual career or the fate of future technology out of your mind. The extracurricular programs kids sign up for need to be fun! Industry leaders like Real Programming 4 Kids make their courses revolve around teaching students to create their own video games.
Kids don’t need to be pushed very hard to play video games, and they are just as drawn to programming them. They can play the games with friends and family after, which is a big motivator.
The best online coding courses also harness gamification dynamics in the sessions, so the same things that make games so addictive and engaging for kids are used for learning.
Credit: Mati Mango via Pexels
Even the best teachers teaching the most engaging subject will struggle if there are too many students packed into a classroom. This is true in online and offline classrooms.
Look for a program that limits class sizes. Four is a great cut-off number, so there is only your child and, at most, three other students. Teachers shouldn’t have to deal with classroom management issues, and students shouldn’t contend against teachers who can’t remember every student’s name because there are so many.
Ideally, the program hires teachers who also grew up playing computer games, as their passion and first-hand experience reach students. Plus, they also have practical experience navigating the job market as a coder, and older students have someone whose brain they can pick about where coding can take them later on.
Learning how to code teaches many useful general computer skills and even fundamental math concepts, like integers, vectors, and trigonometry. Still, kids must also learn the direct skills powering today’s most popular apps, websites, and video games. Employers expect the people they hire to know these languages, and this knowledge lets kids forge their own paths in whatever direction they like.
Here’s a list of the coding languages elite programs teach:
Learning how to write computer code teaches kids how to use lateral thinking like an engineer, problem-solve, and other intangible mental habits. However, nothing replaces knowing the specific languages needed to make programs work.
Society needs to keep up the torrential pace of technological innovation, and parents are looking for fun, beneficial extracurricular activities for their kids today. Even if your child never becomes a professional video game developer or programmer, they’ll be excited to learn and play in a safe, stimulating environment every week. And maybe after they advance in coding, they will develop the next multimillion-dollar video game or generation-defining technology.
Hi learners! I hope you are having a good day. In the previous lecture, we saw Kohonen’s neural network, which is a modern type of neural network. We know that modern neural networks are playing a crucial role in maintaining the workings of multiple industries at a higher level. Today we are talking about another neural network named EfficientNet. It is not only a single neural network but a set of different networks that work alike and have the same principles but have their own specialized workings as well.
EfficentNet is providing groundbreaking innovations in the complex fields of deep learning and computer vision. It makes these fields more accessible and, therefore, enhances their range of practical applications. We will start with the introduction, and then we will share some useful information about the structure of this neural network. So let’s start learning.
EfficientNet is a family of neural networks that are part of CNN's architecture, but it has some of the latest and even better functionalities that help users achieve state-of-the-art efficiency.
The efficientNet was introduced in 2019 in a research paper with the title “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.” Mingxing Tan and Quoc V. Le introduced it, and this is now one of the most popular and latest neural networks. These are Google’s AI researchers, and the popularity of this neural network is due to its robust applications in multiple domains.
The motivation behind EfficentNet's development is the popularity of its parent neural network, CNN, which is an expensive and efficient network. The deployment of CNN in resource-constrained environments such as mobile devices was difficult, which led to the idea of an EfficentNet neural network.
The EffcinetNet has a relatively simpler working model than the CNN to provide efficiency and accuracy. The basic principle of working is the same as in CNN, but EfficeintNet archives better goals because of the scaleable calculations. The convolution of the dataset allows EffcientNet to perform complicated calculations more efficiently. This helps EffcientNet a lot in the processing of images and complex data, and as a result, this neural network is one of the most suitable choices for fields like computer vision and image processing.
As we have mentioned earlier, EffcientNet is not a single neural network but a family. Each neural network has the same architecture but is slightly different because of the different working methods. Some parameters are important to understand before knowing the difference between these members:
When the topic is a neural network, the FLOPs denote the number of floating points per second a neural network can perform. It means the total number of billions of floating point operations an EffcinetNet member can perform.
The parameters define the number of weights and biases that the neural network can learn during the training process. These are usually represented in millions of numbers, and the user must understand that the 5.3 parameter means the particular member can learn 5.3 million parameters it can train.
Accuracy is the most basic and important parameter to check the performance of a neural network. The EffecntNet family varies in accuracy, and users have to choose the best one according to the requirements of the task.
Different family members of EffcientNet are indicated by numbers in the name, and each member has a slightly larger size than the previous one. As a result, accuracy and performance are enhanced. Here is the table that will show you the difference among these:
| Member Name | FLOPs | Parameters | Accuracy |
B0 |
0.6 |
5.3 |
77.1% |
B1 |
1.1 |
7.8 |
79.1% |
B2 |
1.8 |
9.2 |
80.1% |
B3 |
3.2 |
12.0 |
81.6% |
B4 |
5.3 |
19.0 |
82.7% |
B5 |
7.9 |
31.0 |
83.7% |
B6 |
11.8 |
43.0 |
84.4% |
B7 |
19.8 |
66.0 |
84.9% |
This table shows the trade-off between different parameters of EffcientNet models, and it shows that a larger size (increased cost) can be more useful and accurate, and vice versa. These eight members are best for particular types of tasks, and while choosing the best one for the particular task, some other kinds of research are also important.
The workings and structure of every family member of EffcientNet are alike. Therefore, here is a simple and general overview of the features of EffcientNet. This will show the workings and advantages of the EfficientNet neural network.
One of the most significant features of this family is the compound scaling, which is different from other options for neural networks. It has the power to maintain the balance between the following features of the network:
As a result, the EfficientNet network does not require additional computation and provides better performance.
A difference between the traditional CNN and EffientNet neural networks is the depthwise separable convolutions. As a result, the complexity of this network is less than CNN's. All the channels use a separate convolutional kernel; therefore, depthwise separate convolutions are applied to the channels.
The resultant image is then passed through a pointwise convolution. Here, the outputs of the depthwise convolution channel are combined into a single channel. The standard convolution requires a great deal of data, but this technique requires a smaller number of parameters and significantly reduces the complexity.
The EffcientNet family uses a different and more recent type of convolution known as MBConv. It has a better design than the traditional convolution. The depthwise convolutions and pointwise linear convolutions can be done simultaneously. It is useful in reducing floating-point operations for overall performance. The two key features of this architecture are:
Here is a simple introduction to both:
The inverted bottleneck has three main convolutional layers:
This is applied during the computation of the inverted bottleneck. This adds the shortcut connection around the inverted bottleneck, and as a result, the inverted residual blocks are formed. This is important because it helps reduce the loss of information when convolution is applied to the data.
The representational power of EffcientNet can be enhanced by using an architecture called Squeeze and Excite, or SE. It is not a particular or specialized architecture for EfficinetNet but is a separate block that can be incorporated into EfficentNet. The reason to introduce it here is to show that different architectures can be applied to EfficnetNet to enhance efficiency and performance.
The efficeintNet is a family, and therefore, it has multiple sets of workings out of which, the user can choose the most accurate. The eight members of this series (E0 to E7) are ideal for particular tasks; therefore, these provide the options for the user to get the best matching performance. All of these provide a different type of combination of accuracy and size, and therefore, more users are attracted to them.
Hence, this was all about EffientNet, and we have understood all the basic features of this neural network. The EffenctNet is a set of neural networks that are different from each other in accuracy and size, but their workings and structures are similar.
EffcientNet was developed by the Google AI Research team, and the inspiration was CNN. These are considered the lightweight version of the convolutional networks and provide better performance because of the compound scaling and depthwise convolutions. I hope it was helpful for you and if you want to know more about modern neural networks then stay with us because we will talk about these in the coming lectures.
DevOps engineers have a challenging job to do. They are responsible for managing servers, code, and many other components in a software project. They routinely employ numerous tools and calculators to facilitate their daily activities.
Engineers working in DevOps employ a range of calculators to reduce the complexity of any given issue. These tools facilitate and speed up the work. Planning, risk management, and performance optimization can all benefit from them.
Here are some tools that can be useful to a DevOps engineer.
Time is money! Estimating how long a task will take is crucial. This calculator is used by DevOps engineers to estimate various calculations. This tool aids in making time estimates for projects. To predict the amount of time needed in the future, they analyze historical data.
In every field, a budget estimate is required. DevOps engineers use this tool to control the budget because of this. They have a fantastic choice in using this cost calculator to choose an acceptable ratio of cost to keep inside the budget.
Performance is important in all facets of life. CPU load calculators are used to determine the load on a server. They help DevOps engineers decide if more servers are needed. Or maybe they can improve the ones that are already in place. It encourages hasty decision-making.
Data travels swiftly. How rapidly should it progress though? The main purpose of bandwidth calculators is to address this issue. DevOps engineers can use this tool to calculate the amount of bandwidth needed for effective operation. They are necessary to stop system idleness.
Data must be kept in large quantities to prevent loss. These math calculators have been programmed to address this issue. Storage calculators give a precise picture of the amount of space needed. They forecast how much room would be needed over time. The fear of running out of storage is less likely as a result.
To quantify the risks, DevOps engineers employ risk assessment tools. This enables them to combat several unforeseen circumstances that can jeopardize their business. They estimate the likelihood of different dangers.
Return on investment (ROI) is a crucial concept. This calculator is used by DevOps engineers to calculate the benefits of an investment. Like other jobs, measuring the investment or its rewards is crucial. They compare and contrast the costs and benefits. This helps to justify the price of new machinery or systems.
A DevOps engineer is using this tool to calculate the latency in data transport. This helps them identify the problem causing the data transfer delay so that it may be properly repaired. Understanding network delay can help you provide a better user experience.
Hi there! I hope you are having a great day. The success of the field of deep learning is due to its complex and advanced neural networks. These networks can be broadly divided into traditional and modern neural networks. We have seen the details of traditional neural networks, and in the previous session, the basic introduction of modern neural networks and the details of their features were discussed. Today, we will talk about one of the most famous modern neural networks, the Kohonen Self-Organized Neural Network.
Modern neural networks are more organized and developed than traditional neural networks, but that does not make traditional neural networks less efficient than modern ones. All the networks are introduced for specific tasks, and this is one of the main reasons behind the evolution of deep learning in every field. The details of Kohonen's Self-organizing Neural network will prove it, so let’s start learning.
The Kohonen Self-organizing network is also known as the self-organizing feature map (SOFM), and It was developed by Teuvo Kohonen in the 1980s. It is a powerful type of unsupervised learning, the main purpose of which is to map the high dimensional input data even at the lower dimensional grid. It can be used on two or more dimensional data where the neurons are connected and each layer is weighted according to the calculations.
Throughout the data dimensions, the topological properties of the data saved in them remain preserved. During the training process, the self-organizing map learns to organize itself with similar data points and creates a connection with the nearby neurons of the grid.
The training process for SOMs uses competitive learning methods. Think of the scenario where, when new data is added to the network, a quick calculation is made to find the neuron with the same data weight. The most suitable neuron is called the best matching unit (BMU), and adding the new data stimulates it. As a result of this addition, the weights of BMU and their neighbors are updated according to the data. It makes all the neurons similar to each other, and as a result, the network becomes better with time. Here are the details of the key features that we have just discussed:
Topology preservation is the feature of the algorithm that maintains the spatial relationship and the structure of the data that it uses. This all happens when the data is mapped on the lower dimensional grid.
The basic objective of topology preservation is to maintain the structure of the map. This feature preserves the data when it is mapped from higher to lower dimensional space.
This is the basic feature of the Kohonen neural network. The data is arranged in the form of a grid of nodes and neurons. Each of these represents a specific region or cluster of the input data. It becomes easy to maintain the structure of neurons with similar sizes and properties.
This is another way to organize the data in the SOM, and here, the BMU plays a vital role. This feature is responsible for checking two important parameters throughout the processing:
Learning rate
Neighbourhood operation
Here, the learning rate defines the magnitude of the update rate of neurons, and neighborhood operation means the measure of the change in the properties of neighboring neurons when new data is introduced in the model.
Competitive learning helps the network in processes like clustering and visualization. The network autonomously discovers the inherited structure without any need for supervision. It is an iterative process that helps the network grow and learn at a rapid rate.
Understanding the advantages of using Kohonen’s self-organizing network will clarify the significance of this network. Here are some important points about it:
Once you have understood the applications, you are ready to learn about the industrial uses of Kohonen’s self-organizing neural network. The workings of SOM are so organized and automatic that many industries rely on them for the most sensitive calculations, and their results affect the overall performance of that industry. Here are some examples:
The analysis of complex datasets by data mining companies is an important task. Many companies use SOM for such processes where the patterns have to be observed carefully to provide detailed analyses. Different techniques are useful in this regard, but SOM is used here because of the organized pattern and competitive learning.
Some of these companies provide tools for data exploration to their clients. Some provide customer segmentation and anomaly detection. All of these require the use of powerful neural networks, and they use SOM along with other networks for this.
In industries where financial records are imported, this technique detects fraud. For instance, it identifies the patterns of stock marketing and helps detect any abnormal bhavior. In addition to this, processes like risk assessment and credit storage are improved with the help of SOM. This is done in the institutes that are working globally, and a large community has to be handled by the institutes.
The advancement in technology has provided multiple advantages, but it has also led to increased security risks. The SOM is helpful in dealing with such issues. Here are some points to justify how SOM is helpful in different types of technical crimes:
Hence, today we have seen the details of Kohonen’s self-organizing neural network. It is a type of modern neural network that is helping people in different applications in real life. We have seen the features and workings of this neural network, and to understand its importance, we have seen its applications and advantages at different levels. I hope it was helpful to you, and if you want to know more types of modern neural networks, then we will discuss these in the coming sessions. Happy learning.
In a world captivated by the allure of cutting-edge technology and automation, it's easy to overlook the enduring importance of skilled trade jobs . Yet, these professions are not just surviving, they're thriving, as the demand for trade experts is not only robust but also projected to soar in the years to come.
Offering lucrative salaries, rock-solid job security, and abundant opportunities for career growth, trade jobs are the unsung heroes of the modern workforce.
In this article, we'll lift the curtain on 10 high-paying trade jobs of the future and give you the inside scoop on how to make one of these rewarding careers your own.
As the world races to embrace cleaner, greener energy solutions, solar photovoltaic installers are the trailblazers leading us into a sun-powered future. These eco-warriors are the hands and brains behind the installation, maintenance, and repair of solar panel systems that grace rooftops and open fields alike.
With a jaw-dropping projected growth rate of 63% between 2019 and 2029 , according to the U.S. Bureau of Labor Statistics, this career is not just hot—it's sizzling!
So, how do you join this solar brigade? You've got options! You can dive right in with on-the-job training or sharpen your skills at a technical school offering specialized courses in solar technology.
IMG Source - https://unsplash.com/photos/PU9Z6n761bc
However, if you're aiming for the top, some states require certification—a badge of honor that can be yours after taking an exam. As for the paycheck, the average solar photovoltaic installer earns around $44,890 annually. But hold onto your hard hat—seasoned pros in this field can rake in significantly more!
As the world's appetite for renewable energy grows, wind turbine technicians are the daredevils riding the gusts of this booming industry. These adrenaline junkies don't just clock in and out, they scale towering turbines and battle the elements to ensure these wind-powered behemoths are in top shape.
If you're wondering about job security, here's a number to blow you away: a staggering 61% growth rate is expected for this role between 2019 and 2029. Talk about a career that's literally on the rise!
So, how do you catch this updraft? First, you'll want to earn your wings at a technical school that offers specialized training in wind energy technology, but the learning doesn't stop there, as you'll also get hands-on experience through on-the-job training.
As for the financial rewards, the average wind turbine technician earns a breezy $56,230 per year, with plenty of room for growth as you gain experience.
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In a world where almost everything runs on electricity, electricians are the unsung heroes keeping the lights on—literally. From powering your cozy home and bustling workplace to ensuring that industrial machinery hums along smoothly, these masters of the circuit are indispensable.
As we venture further into the age of smart homes, electric vehicles, and automation, the demand for specialized electricians is set to surge like a bolt of lightning. Thus, it is no surprise that electricians take home over $57,000 on an average each year, with the role set to see a 6% YoY increase in openings.
The journey here often begins with an apprenticeship program that blends together classroom learning, along with on-the-job training. Electricians stand to earn substantially more if they choose to specialize, such as becoming an industrial electrician, or an oil rig electrician, among other things.
In a world obsessed with digital wizardry, plumbers are the timeless guardians of our most basic needs—clean water and functional sanitation. Whether it's laying down the plumbing foundation for a skyscraper or rescuing a suburban home from a disastrous leak, plumbers are the unsung heroes.
This is also one of the few professions on this list that is virtually recession-proof , because no matter how dire the global economy gets, people will not put off a plumbing emergency to a later date. It also helps that plumbing is one such trade that cannot be either outsourced or automated.
So, how do you dive into this evergreen profession? Most aspiring plumbers kick start their careers with an apprenticeship, where they earn while they learn. Plumbers who are licensed can see floodgates of opportunities open for them, allowing for average pay as high as $55,000 annually.
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As the skies fill with more planes, the unsung heroes ensuring high quality assurance standards
, so that we can fly safely are none other than aircraft mechanics.
These skyward engineers are the last line of defense between us and aviation disasters, meticulously maintaining, diagnosing, and repairing the mechanical marvels that are modern airplanes. With the aviation industry set to soar, the demand for these high-flying professionals is set to take off with it.
Your first stop in this business is an FAA-approved Aviation Maintenance Technician School, where you'll learn the A to Z of aircraft maintenance. But that's not all, you'll also need to ace certification exams to prove you've got the right stuff. Once you’re done with this, you can expect average pay in excess of $64,000 a year.
You wouldn’t expect to see diesel mechanics among the most lucrative trades of the future, but believe us when we say this, diesel engines are not going anywhere.
While they will certainly be phased out among most passenger cars over the coming years, they will continue to dominate when it comes to marine engines, locomotives, and construction and farming equipment for the foreseeable future.
This makes it a much more specialized job, requiring a certification from the National Institute for Automotive Service Excellence (ASE), with the best paying diesel mechanic jobs paying well in excess of $58,000 a year, and with plenty of scope for advancement.
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As we navigate a world increasingly shaped by technology and automation, it's easy to overlook the enduring value of skilled trades. But make no mistake—these professions aren't just holding steady, they're set to flourish in the coming years.
Whether you're fresh out of high school or contemplating a career pivot, there's a high-paying trade job with your name on it. All you need is a few years of dedication, and a can-do attitude to get things rolling, and a lucrative career can be yours, with no student loan debt, or any other prerequisites.
In the subject of electrical engineering, having precise calculations is essential to the process of creating systems that are both effective and safe. One such calculation that is extremely significant is called the voltage drop, it refers to the decrease in voltage that occurs when electric current flows through a circuit.
A great number of electrical engineers rely on a voltage drop calculator to ensure that the circuits they design match the necessary criteria. This page provides an overview of the many approaches to calculating voltage drop, making it simpler for engineers to select the strategy that is most suited to their individual projects.
It is vital to have a solid understanding of the voltage drop formula before moving on to the automated approaches. In a DC circuit, the voltage drop, denoted by "V d," can be determined using the following formula:
Where I denote the current and R the resistance, respectively.
When used in an AC circuit, the formula gets more complicated because it must take into account additional parameters, such as power factor and impedance. These factors can be handled by a voltage calculator, but it is vital to grasp the underlying formula in order to troubleshoot and gain a greater understanding of the topic.
A good number of engineers construct their own dc voltage drop calculators by using spreadsheet programs like Excel. The flexibility to tailor the calculator to meet one's own needs is one of the many benefits offered here.
You may simply determine the voltage drop for a variety of circuit designs by entering the relevant circuit variables. In addition, you have the option of utilizing conditional formatting in order to highlight any numbers that are beyond the allowed thresholds.
A voltage drop calculator can be accomplished through the use of a number of specialist software applications. These tools have a graphical user interface that is easier to navigate and is capable of analyzing more complicated circuits, such as those with several branches and components. It is common for the program to have additional features, such as load analysis and component sizing, which enables it to serve as a one-stop solution for electrical design.
There are free voltage drop calculators available on a variety of websites, which can be used to perform calculations quickly. These tools are helpful for performing a quick check.
In spite of this, they offer a straightforward and speedy method for obtaining approximate findings, which is especially useful when time is of the essence.
Calculating voltage drop is an essential task, and there are many different approaches that may be used to do this. A DC voltage drop calculator can be a helpful tool for practical applications, while the voltage drop formula provides the theoretical underpinning for the topic.
Manual calculations, spreadsheets, specialist software, and internet calculators are the various options available to engineers who are looking for the most appropriate way to meet their requirements.
Financial security is one of the fundamental attributes of happiness, as it allows one to feel free in the ever-changing world. However, making a decent living goes beyond standard working conditions, and requires more prompt actions, like investments. One of the most accessible forms of investments is Forex trading, as you can start even when having modest financial funds, yet, decent knowledge on the matter. If you are ready to take decisive steps toward a better future, let’s clarify what you need to know about Forex Trading.
Forex trading, or foreign exchange currency trade, is a kind of financial investment, where the traders choose the currencies and try to predict their behavior against each other - increase or decrease in the rate. Depending on the correctness of the predictions they either earn or lose money.
The basic unit of the Forex exchange trade is a currency pair, where there are two types of currency - base and quote. The ratio of the currencies shows how much of a quote currency is needed to buy one unit of the base one. Basically, the same principle as if you were exchanging money at the exchange point when traveling. Yet this time you buy and sell currency aiming to earn money, not for the sake of exchange.
There are six main currencies that rule the global market and are traded in high volumes: USD (US Dollar), EUR (Euro), GBP (Great British Pound), JPY (Japanese Yen), and CAD (Canadian Dollar).
Currency pairs can be divided into majors, minors, and exotics.
The examples of the major pairs are GBP/USD, EUR/USD, USD/JPY, USD/CAD, and AUD/USD.
Minor pairs - EUR/GBP, GBP/CAD, CAD/JPY, AUD/CAD, CHF/JPY.
The major difference is the liquidity of the currency. Majors are more popular, and thus, more liquid. Besides, majors don’t have dramatic changes in exchange rates. Minors, on the other hand, have a lower level of liquidity, thus, the trading cost and volatility are higher.
Exotics is the combination of major currencies against ones from a developing market. Examples. USD/IDR (Indonesian Rupee), USD/HKD (Hong Kong Dollar), USD/ZAR (South African Rand), USD/THB (Thai Baht), USD/UAH (Ukrainian Hryvnia), USD/NOK (Norwegian Krone), USD/SGD (Singapore Dollar).
Those who are willing to trade the exotics need to understand that smaller markets can easily be destabilized, which causes extreme volatility.
Approaching Forex trade without proper insight into the way things work is the right way to lose the funds. Forex is a global market, therefore, the trading success will depend on the forex knowledgeability (strategies, functions, risk management) and the awareness of global events, as long as they give an impulse to numerous trends and movements within global society, which instantly influences the currency movements.
The next important step is the choice of a broker and the platform.
The broker needs to adhere to the law and regulations, otherwise, your funds will not be protected enough. Check for the licenses from regulators like FCA, BaFin, MAS, ASIC, NFA, and CFTC, as their authority is accepted globally.
Brokers provide access to a platform that makes buying and selling currencies possible. There are two types of platforms - MetaTrader4 and MetaTrader5.
MetaTrader4, or mt4, is a good choice if you want to keep things simple and trade forex and CFDs (Contract for difference). MetaTrader 5, or mt5 pc , is created with a more updated programming code, which provides a better speed compared to mt4. Besides, if you are planning on trading stocks and more than one asset, then mt5 will definitely be a better choice.
After choosing a reliable broker, you need to create an account, make the first deposit, and start the trade. The starting deposit depends on the broker and may start even from $5.
However, it is highly recommended to use a demo account first, as it allows you to trade using virtual assets, with no need to use real funds.
Some brokers offer welcome bonuses, or referral bonuses, which is also a worthy option, as you have more chances to trade without spending your own money.
Forex Trade opens a whole world of financial opportunities. It is one of the best forms of financial investments nowadays, however, the initial stage of participation may seem rather daunting due to the large amount of new information and things to learn. Therefore, it is important to be patient and observant. Learn the theory, practice the strategies, be vigilant about the current events in the world, and use all your knowledge when analyzing the currency market. The result will be formed into accurate trading predictions, and thus, your profit and financial well-being.