Neural networks belong to the family of artificial intelligence and machine learning technologies. Various industries and enterprises are already employing these advanced technologies and concepts to gain a market edge. Fields such as banking, retail, healthcare, marketing, and manufacturing use AI-based systems and applications.
Studying neural networks may require significant effort, and the best way to master these skills is to practice. One of the practice options that can help understand the mechanisms of action is to learn how to create a neural network. You can explore the world of these models and create innovative systems. You can combine neural networks with other advanced technologies and concepts.
What Exactly is a Neural Network?
A neural network project involves designing, implementing, and training artificial neural networks to solve specific problems or tasks. Neural networks are a type of machine learning model that can learn from large datasets to make predictions or classifications and can be applied in various areas such as computer vision, natural language processing, and robotics. They often involve complex algorithms, data preprocessing, and hyperparameter tuning to achieve optimal performance.
Three examples of neural networks include convolutional neural networks (CNNs), used for image recognition and classification; recurrent neural networks (RNNs), for natural language processing and sequence prediction; and generative adversarial networks (GANs), used for creating realistic images and data. Online neural networks are easier to make in the format of an RNN.
What Role Does an Artificial Neuron Play?
The very first step to understanding what an artificial neural network does is to understand a neuron. Neural networks mimic the actual neurons of the human brain, hence the name. Neurons have branches protruding from both ends, called dendrites. One cannot do much, but when thousands of neurons connect and work together, they become powerful and can process complex actions and concepts. A computer node works just like a human neuron.
Concept: Independent values (input signals) pass through a “neuron” to generate a dependent value (output signal). Whatever we hear, smell, touch, whatever it might be, is processed as an input layer and then sent out as output. For our digital neuron, independent values (input signals) pass through a “neuron” to generate a dependent value (output signal).
Why Do We Need Neural Networks?
Six examples of neural networks in our daily lives include:
- Voice assistants like Siri and Alice.
- Image recognition in social media and security systems.
- Personalized recommendations in online stores and streaming services.
- Language translation in chatbots and language learning apps.
- Fraud detection in financial transactions.
- Creation of textual and graphical content.
Neural Networks in IT
In the IT field, neural networks simplify project creation. Let’s consider a few illustrative examples.
- Cryptography. You can develop an application for encryption and decryption using state diagrams and tables. The output will include encrypted/decrypted output.
- The financial sector has changed significantly in recent years due to the introduction of advanced technologies and digital systems. You can use them to develop an intelligent credit scoring system for banks. These applications can offer improved predictive capabilities with higher accuracy and classification capabilities. In the initial phase, you must extract real credit information for detailed analytics.
- Automated music generation system. You can create real music without any basic knowledge of playing instruments. You can develop these systems for leisure or professional music creation. MIDI file data can be used to develop these applications, and you can also create an LSTM model to create new and interesting compositions. With deep models, you can program different methods for learning and discovering a wide range of patterns. They can include various musical styles and harmonies.
- Forecasting the stock market value using RNN. Recurrent neural networks, RNNs, can handle sequences of various lengths. You can predict the stock value of any organization using stock price history, company data, and market data. Sequences can be used to determine the price of stocks, short-term and long-term statistics.
How Neural Networks Are Structured
Neural networks are parallel information processing systems consisting of a series of simple neurons (also called nodes or units) organized in layers and connected by links. Artificial models mimic the interconnected structures of animal and human brains and nervous systems, where neurons correspond to cell bodies, and connections are equivalent to axons in biology.
Input variables are fed into input blocks, which distribute information through connections. Thus, input variables are multiplied by the connection weights between the input and hidden layers. Hidden neurons sum the weighted signals from input neurons, then project this sum onto an activation function. The resulting activations of hidden neurons are processed by connections between hidden and output and sent to output neurons. The output neuron also performs a summation and projection of its activation function. Next, we’ll look into where one can create a neural network.
The Theory of Creating Artificial Intelligence
Let’s consider how to create a neural network in python using autoencoders. Autoencoders are the simplest forms of deep learning architectures. In these networks, the input first gets compressed into a low-dimensional code. The next step involves reconstructing the output using the compact code representation. Autoencoders are also called feedforward neural networks. This is because they have three separate components built into them. The encoder, code, and decoder—these are the three components.
In these neural networks, input goes through the encoder and then generates the code. Then this code is used by the decoder to process the output. The output is produced in this process, and it is identical to the input. To develop the project, you will need an encoding method, a loss function, and a decoding method. We suggest you use binary cross-entropy or mean squared error as the two options for the loss function. The method of backpropagation can be used to train autoencoders.
Developing neural network architectures from scratch for solving more complex tasks is typically done by entire research teams, and making a complex neural network on one’s own is practically impossible. However, there are some things to keep in mind. Deeper networks have greater abstraction but also higher complexity.
