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Introduction to Neural Network Training in Artificial Intelligence

Artificial intelligence (AI) is a rapidly evolving field that looks to create intelligent machines capable of performing tasks that typically require human intelligence. It’s not about creating robots that look and act exactly like humans, but rather about machines that can learn, reason, and make decisions on their own.

AI is a tool that we can use to automate tasks, solve complex problems, and improve our lives in countless ways. From self-driving cars and medical diagnosis to fraud detection and financial forecasting, AI is already having a profound impact on our world. As such, to help AI make intelligent decisions and continue to grow and expand its perspective, we train it using a powerful method called Neural Network Training.

What is a Neural Network?

Neural networks are artificial intelligence inspired by the structure and function of the human brain. They are made up of interconnected nodes, or neurons, that can process information and learn from experience like we all did in our childhood.

Figure on the left: The biological neuron graph & on the right: the artificial neural network

Parts of a Neural Network

A neural network is made up of several parts, but the main ones are as follows.

Neuron —a basic building block of a neural network. It takes weighted values, performs mathematical calculation, and produces an output. It is also called a unit, node, or perceptron.
Input — the data/values passed to the neurons.
Deep Neural Network (DNN) — an Artificial Neural Network with many hidden layers (layers between the input (first) layer and the output (last) layer).
Weights — values that explain the strength (degree of importance) of the connection between any two neurons.
Bias — a constant value added to the sum of the product between input values and respective weights. It is used to accelerate or delay the activation of a given node.
Activation function — a function used to introduce the non-linearity phenomenon into the Neural Network system. This allows the network to learn more complex patterns.

How Neural Network Performs

An artificial neuron takes one or more input values with weights assigned to them. The weighted inputs are summed up, and an activation function is applied to get the results inside this node. The output of the node is passed on to the other nodes or, in the instance of the last layer of the network, the output is the overall output of the network.

Neural networks are trained by feeding them large amounts of data. The network will then adjust the connections between its neurons to improve its task performance. This process is called backpropagation. Once trained, neural networks can be used to solve a broad variety of problems.

Process of Neural Network Training

1. Data Collection: The first step in training a neural network is to collect a large amount of data. This data can be anything from images and text to sound and sensor readings.

2. Data Preprocessing: The data must then be preprocessed before it can be used to train the network. This may involve cleaning the data and scaling it to a consistent format.

3. Network Architecture Selection: The next step is to choose the architecture of the neural network. This includes the number of layers, the number of neurons in each layer, and the connections between the neurons.

4. Training the Network: The network is then trained by feeding it the preprocessed data. The network will adjust the connections between its neurons to improve its task performance.

5. Evaluation and Refinement: Once the network is trained, it is evaluated on a set of test data. This data is not used to train the network, output is used to assess the network’s accuracy. If the network does not perform well on the test data, it can be re-trained with more data or a different architecture.

When it comes to neural network training, the main types can be categorised based on two factors, namely, learning paradigm and network architecture.

Neural Network Training Types

Below are just the main types, and many other variations and combinations that can be used to train neural networks. Choosing the right type of training and network architecture depends on your specific problem and data.

Network Architecture:

Feedforward neural networks:

Information flows in one direction, from the input layer through hidden layers to the output layer. These are versatile and well-suited for a variety of tasks. These types of networks used in recommendation systems power many of the suggestions you see online, from product recommendations on shopping websites and image & speech recognition.

Recurrent neural networks (RNNs):

These networks are designed to handle sequential data, like text or time series data. They have loops that allow them to remember information from earlier inputs. Commonly used in translating text from one language to another, generating creative content like articles, weather forecasting, traffic prediction, and to help identify and label objects in videos, and music generation.

Convolutional neural networks (CNNs):

These networks excel at tasks involving image recognition and processing. They use special layers called “filters” to extract features from images. CNNs are used in self-driving cars to perceive their surroundings, identify objects like pedestrians and vehicles, and navigate safely on roads, they can also be used to analyse customer behaviour, and optimise product placement, and analyse X-rays, MRIs, and other scans that detect tumours, diagnose diseases.

Generative adversarial networks (GANs):

These involve two competing networks - a generator that creates new data and a discriminator that tries to distinguish it from real data. The competition improves the quality of both networks. GANs can generate incredibly realistic and creative images and videos for us based on textual descriptions. In addition, used in image editing and manipulation as well.

Learning Paradigm:

Supervised learning:

This involves training the network with labelled data, where each input has a corresponding desired output. The network learns to map the input to the desired output by adjusting its internal parameters. This is the most common type of training, we use for tasks like classification, regression, and forecasting.

Supervised learning is a way that we can teach computers to do things by showing them examples and telling them the right answer. For example, let’s say we want to teach a computer to recognise the name of fruits. We can show it pictures of different fruits and tell it the name of each fruit. Then the computer will try to figure out which characteristics typically go with each fruit.

Once the computer has learned enough about different fruits, we can test it by showing it a picture of a fruit that it has never seen before. The computer will use what it has learned to try to guess the name of the fruit. If we’re guessing correctly, we can say that the computer did a good job of learning about fruit. If it doesn’t guess correctly, we can give it more examples to help it learn even better.

Types of Supervised Learning Algorithm

Below are 2 types of algorithms that can be used for supervised learning.

- Regression: Regression is a supervised learning technique used to predict continuous numerical values based on input features. It aims to prove a functional relationship between independent variables and a dependent variable, such as predicting house prices based on features like size, bedrooms, and location. The goal is to minimise the difference between predicted and actual values using algorithms like Linear Regression, Decision Trees, or Neural Networks, ensuring the model captures underlying patterns in the data.

- Classification: Classification is a type of supervised learning that categorises input data into predefined labels. It involves training a model on labelled data examples to learn patterns between input features and output classes. In classification, the target variable is a categorical value. For instance, classifying emails as spammy or not. The model’s goal is to generalise this learning to make accurate predictions on new, unseen data. Algorithms like Decision Trees, Neural Networks, and Support Vector Machines are commonly used for classification tasks.

Unsupervised learning:

In this method, the network is given unlabelled data and tasked with finding patterns or structures within it on its own. This is useful for tasks like dimensionality reduction, data clustering, and anomaly detection.

Here, we give the network unlabelled data, which are not categorised, and the corresponding outputs are also not given. Then, this unlabelled input data is fed to the machine learning model to train it. Firstly, it will interpret the raw data to find the hidden patterns within the data and then will apply suitable algorithms including SVM (Support Vector Machine), k-means clustering, decision tree, etc.

In this example, we show a mix of images of cats and dogs to the computer, then based on the unique features of the cat’s and dog’s computer group the image.

Some of the unique features that the computer can use are:

Ratio of length of tail to body height. Cats will have a higher value for this.
Distance between eyes
Length of ear

Once it applies the suitable algorithm, the algorithm divides the data objects into groups according to the similarities and differences between the objects.

Types of Unsupervised Learning Algorithm

- Clustering: Clustering is a method of grouping objects into clusters such that objects with the most similarities remain in a group and have less or no similarities with the objects of another group. Cluster analysis finds the commonalities between the data objects and categorises them as per the presence and absence of those commonalities.

- Association: An association rule is an unsupervised learning method used for finding relationships between variables in a large database. It figures out the set of items that occur together in the dataset. Association rules make marketing strategy more effective. For instance, people who buy X items (suppose bread) also tend to buy Y (Butter/Jam) items. A typical example of an Association rule is “Market Basket Analysis”.

Reinforcement learning:

The network learns through trial and error, interacting with an environment and receiving rewards for desired behaviours. This is often used for robotics, game playing, and other complex tasks. It is very similar to training our children by giving them feedback. When children do good work, we give good feedback and gifts then they tend to continue it. If the child does a bad thing, we give negative feedback and punishment then they will avoid it next time.

Types of Reinforced Learning

Positive Learning: Positive Learning is defined as an event that occurs because of a specific behaviour. It increases the strength and the frequency of the behaviour and positively impacts the action taken by the agent. This type of Reinforcement helps you to maximise performance and sustain change for a more extended period. However, too much Reinforcement may lead to over-optimisation of the state, which can affect the results in a negative manner.

Negative Learning: Negative Reinforcement learning is defined as the strengthening of behaviour that occurs because of a negative condition that should have been stopped or avoided. It helps you to define the minimum standard of performance. Despite the positives, a drawback of this method is that it provides enough to meet up the minimum behaviour.

Summary

Neural networks can be used in many areas such as facial recognition, stock market prediction, aerospace engineering, defence, healthcare, signature verification, etc. Compared to human decision -making, Neural networks are very accurate, but it depends on the amount of data we use to train the network. The more you train the network will give you more exact results. However, finding a valid large amount of data will be challenging for neural network training. In summary, while neural network training offers powerful capabilities for solving complex problems, it also presents challenges related to data requirements, model complexity, computational resources, and interpretability.