As Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) continue to transform our daily lives, it’s essential to understand the nuances that set them apart. While these terms are often used interchangeably, DL is a distinct subset of ML that uses neural networks to replicate the human brain’s ability to process complex information and respond accordingly.
The role of neural networks.
Neural networks are a type of AI that imitate the structure and function of the human brain, and are widely applied in the fields of machine learning, AI, and deep learning to tackle intricate problems. Although Artificial Neural Networks (ANNs) and Simulated Neural Networks (SNNs) refer to the same concept, ANNs is the preferred term. A prime illustration of the practical use of neural networks is Google’s search algorithm that employs this technology to detect patterns and arrive at solutions to complex queries.
Neural networks, also known as machine learning subfields, form the core elements of deep learning algorithms, as discussed in our blog on the application of deep learning in quantum chemistry. Just like the synaptic connections between neurons in the human brain, neural networks are made up of linked nodes that enable intercommunication between them. Typically, these nodes are composed of one or more concealed layers and an output layer.
Every simulated neuron in the network is linked to its neighbouring neurons and has an assigned weight and threshold. When the output of a node exceeds the pre-defined threshold, it is activated and the data is transmitted to the succeeding node. Conversely, if the output falls below the threshold, the data will not pass to the following node.
Training data is an indispensable component for the effective operation of neural networks, as it aids in continually enhancing their performance. When precisely trained, these learning algorithms deliver immense benefits for artificial intelligence applications, as discussed in our blog post on the relevance of artificial intelligence in robotics. Additionally, they facilitate speedy sorting and classification of data, thereby enabling tasks like image recognition to be executed in a fraction of the time taken for manual identification.
Neural Networks: Different Varieties
Numerous models of neural networks exist, each with its own distinct purpose, as outlined below.
Utilizing a Single-Layer Perceptron
Frank Rosenblatt is widely regarded as the trailblazer of AI, credited with inventing the first neural network called the perceptron, as discussed in our post on building MLPs through Tensorflow. Despite its rudimentary design, the perceptron was engineered with minimal complexity, containing only a solitary neuron.
Employing Multilayer Perceptrons (MLPs)
The core components of Natural Language Processing (NLP) comprise an input layer, a hidden layer, and an output layer. To tackle non-linear situations that frequently arise in practical settings, Multi-Layer Perceptrons (MLPs) leverage sigmoid neurons in lieu of traditional perceptrons. MLPs are trained and refined using data to generate effective and precise models.
Recursive Neural Networks (CNNs)
Neural networks that utilise matrix multiplication are often similar to Multi-Layer Perceptrons (MLPs). Nevertheless, these networks are mainly used for tasks that pertain to computer vision and pattern recognition, as discussed in our blog on hiring remote computer vision engineers. With the aid of matrix multiplication, these networks can analyse intricate visual patterns.
Utilizing Recurrent Neural Networks (RNNs) That Store Previous Input
In time-series data, such as stock market information, predictions can be made by detecting periodic trends and leveraging them.
The Internal Mechanisms of Neural Networks
The subsequent partitioning of phases outlines the operational process of neural networks:
Neurons
The neuron is the fundamental component of a neural network model, which accepts data as input, analyses and processes it, and generates outputs as a result. Throughout this process, three important stages take place:
- Multiplying each input by a specific weight.
- The sum of all input weights undergoes a bias b addition.
- All of the results are summed up.
We begin by importing numpy as np. We use this library to define the sigmoid activation function, f(x) = 1 / (1 + e(-x)). This can be computed by dividing 1 by (1 + np.exp(-x)). In the Neuron class, initialise the weights and biases using the “init” method, where Weights = self.weights. When biases are considered, this results in prejudice. The “feedforward” method, which is defined as follows, takes in inputs:
The Activation Function is Applied after Input Weights are Multiplied, and Bias is Added.
The sum formula used is defined as np.dot(self.weights, inputs) + self.bias. Then, the total is passed through the sigmoid function. Here’s an example of how to use these formulas:
The weights are defined as np.array([0, 1]), where w1 = 0 and w2 = 1, and the bias is 4, so b = 4. The neuron is initialized as Neuron = n(weights, bias).
To compute the output, an array x is defined as np.array([2, 3]), where x1 = 2 and x2 = 3. The output of n.feedforward(x) is 0.9990889488055994.
Activation functions, such as the sigmoid function, can convert an unbounded input into a bounded one. The sigmoid function is a member of the family of activation functions, and produces only two possible outputs, zero and one, irrespective of the input value, even if it is a negative integer.
Constructing a Network from Individual Neurons
A neural network is formed by connecting multiple neurons together. Typically, each neuron has two inputs, with two neurons in a hidden layer and one in an output layer. The input layers are labelled 0 and 1, and the hidden layer is comprised of h1 and h2 neurons, while the output layer is called O1. Any layer between the input and output layers can be regarded as a hidden layer, and the number of such layers can be infinite.
The number of neurons present in a neural network is not limited by the number of layers present. The fundamental principle remains the same: the algorithm requires inputs to generate the desired outputs.
The Neural Pathways of the Brain and Networking
The process of training and refining a neural network involves minimizing the loss due to mean squared error (MSE). This occurs while attempting to optimize the performance of a neural network by quantifying its effectiveness. The expected output is compared to the actual output, and the difference is calculated, serving as a metric to determine the neural network’s efficiency.
As an example, consider the following formula:
- A collection of inputs comprises N elements.
- When making a prediction, Y is the focus.
- The genuine value of the predictor variable is represented by Y true.
- The anticipated outcome or the value of the variable is indicated by Y pred.
The square of the error represents the difference between the genuine and predicted values of a specific variable, and its computation is performed by employing a loss function. Weight reduction is a crucial consideration when striving to improve the precision of a prediction, as it can serve to mitigate potential financial losses. Therefore, minimizing this loss plays a pivotal role in the training of neural networks.
By modifying the associated weights, it is possible to adjust the predictive power of the network. Following the labelling of each weight in the network, the loss can be represented as a multivariate function.
Through the implementation of stochastic gradient descent, you can learn how to modify the weights to reduce the associated loss.
The learning rate is represented by a constant symbol. To eliminate w1 from w1/L, consider the following:
- If L/w1 is positive, w1 decreases, resulting in a decrease in L.
- If it is negative, w1 increases and L decreases.
By performing this process on each of the network’s weights, it becomes possible to decrease the associated loss and enhance the network’s effectiveness. To ensure the reliability of the training process, it is advised to follow these steps:
- To simulate stochastic gradient descent, pick a single instance from the dataset and complete all relevant operations on it in one go.
- Determining all critical loss derivatives for the scales.
- The update equation is employed to modify the weights.
- Returning to Step 1 and continuing the process.
Having completed all the preceding steps successfully, you are now prepared to create a complete and operational neural network. By following the instructions closely, you can enhance the precision of your network and decrease its loss. Additionally, by executing the code and varying the parameters, you can acquire a deeper understanding of how to enhance the neural network’s efficiency.
