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- 1 A neural network is an artificial intelligence model that trains software to learn and respond to data independently.
- 2 This training can be done through supervised learning, where the software is given various datasets with answers and expected to identify the patterns in them and predict subsequent data from what is learned.
A neural network is an artificial intelligence model that can be trained to enable computer software to perform analytic tasks without supervision. In this article, we discuss what a neural network is in detail and how they are used in crypto.
What Is a Neural Network?
A Neural Network is a type of machine learning model that is structured like the nervous system of the human brain. It enables computer applications to perform analytic tasks (like face recognition, price prediction, and data analysis) without supervision and accurately. It is designed to mirror the nervous system of the brain, which contains billions of neurons that are interconnected. The connections between the neurons change as we learn new behaviors, which enables us to learn from mistakes and respond accurately over time.
How Does the Neural Network Work?
The neural network works by processing data through three layers. Each of these layers is made up of neurons that are connected and send signals to each other
1) Input layer
The input layer is the first layer of the neural network and receives data that is to be analyzed. This could be a picture of a cat for image recognition or even information on a blockchain for the price prediction of its native currency.
The number of neurons on the input layer depends on the function of the neural network. If it is to predict the price of a crypto asset (for instance, the price of ETH) and the features that are to be analyzed are ETH’s tokenomics, whitepaper, and blockchain security, the input layer will have three neurons. Each of these neurons will collect data on a specific feature.
A neuron may collect more data than other neurons on the layer. This will make its data have more influence on the result. If the neuron collecting information on Ethereum blockchain security collects more data than the other two neurons, the final price prediction made by the network will be greatly influenced by how secure the blockchain is. To prevent this, all input data is scaled to ensure that it is in the same range before being passed to the next layer.
2) Hidden layer
The hidden layer is between the input layer, where data is received, and the output layer, where the final result is calculated. It identifies important features in a data set and sends them to the neurons for analysis. It also allows non-linear analysis of data. This means that with the hidden layer, the model can identify and learn from complex patterns and produce more accurate output data.
The hidden layer also allows the model to generalize the data. This means that it can use what is learned from a function to produce an accurate result for the next set of data. So if the neural network were used to predict the future price of ETH, it would use what was learned during the prediction to produce an accurate result when performing another function (like analyzing the Solana blockchain).
3) Output Layer
The output layer is the last layer of the neural network, where calculations based on the analysis made in the hidden layer are done. The conclusion is made on this layer.
Every neural network has one output layer, with its nodes corresponding to the neurons in the input layer. So if the input layer has three neurons receiving data on the tokenomics, whitepaper, and social media hype of a coin, the output layer of the neural network will also have three nodes, each of which represents the result of the input data.
What Are the Types of Neural Networks?
1) Feed Forward Neural Network
In this neural network, the data flows only in one direction, that is, from the input layer to the hidden layer and the output layer. There is no feedback loop; the output data cannot re-enter the input layer and influence the accuracy of subsequent results. The best way to improve the accuracy of a feed-forward network is to increase the number of hidden layers (as this enables the network to identify and learn more complex patterns), increase the number of nodes on the input and hidden layers (this allows the network to receive and process more data), and normalize input data.
2) Recurrent Neural Network
This neural network has a feedback loop so that all or some of the output data re-enters the network in the input layer. By doing this, the network can learn from previous data. Recurrent neural networks also have a hidden state (which is also called their memory state). This stores information on previous input data so that the network can remember and predict the next step accurately.
3) Deep Neural Network
This neural network contains more than three layers (that is, three or more hidden layers), with each layer having multiple neurons. Because of its depth, a deep neural network can detect complex data patterns. By doing this, the network can collect more input data, process it on its multiple hidden layers, learn from complex structures in the data, and produce more accurate output data.
How Are Neural Networks and Deep Learning Models Used in Crypto?
1) It Enables Automated Trading
Automatic trading involves using computer software to execute trade orders at high speed. These programs are designed to follow specific market rules like stop loss and moving averages. By combining neural networks with this trading software, your model can learn from past trades and implement future trades with both speed and accuracy.
2). Market Analysis and Prediction
By using deep neural networks, your model can detect non-linear patterns in market data, learn from these patterns, and improve market analysis. Neural networks also help you identify optimal investment opportunities.
3) Improving Smart Contracts and Crypto Security
AI can run automated checks on smart contracts, detect bugs, and suggest debugging processes. It can also be trained to detect suspicious activity on the blockchain and offer solutions.
Conclusion
The neural network and deep learning models are used to improve market analysis, price predictions, and blockchain security, run automated checks on smart contracts and detect optimal investment opportunities.
Andrew is a blockchain developer who developed his interest in cryptocurrencies while pursuing his post-graduation major in blockchain development. He is a keen observer of details and shares his passion for writing, along with coding. His backend knowledge about blockchain helps him give a unique perspective to his writing skills, and a reliable craft at explaining the concepts such as blockchain programming, languages and token minting. He also frequently shares technical details and performance indicators of ICOs and IDOs.
