Artificial Neural Networks (ANNs)

- Overview

With the development of neural networks, various tasks that were considered unimaginable can now be easily accomplished. Tasks like image recognition, speech recognition, and finding deeper relationships in datasets become easier. Sincere thanks to outstanding researchers in the field whose discoveries and discoveries have helped us harness the true power of neural networks.

Neural networks are the cornerstone of today's technological breakthroughs in deep learning (DL). Neural networks can be thought of as massively parallel simple processing units capable of storing knowledge and applying that knowledge to make predictions.

Neural networks, also known as artificial neural networks (ANNs) or analog neural networks (SNNs), are a subset of machine learning (ML) and are at the heart of deep learning (DL) algorithms. Their name and structure are inspired by the human brain, mimicking the way biological neurons signal each other. 

ANNs are used for various tasks, including predictive modeling, adaptive control, and solving problems in AI. They can learn from experience, and can derive conclusions from a complex and seemingly unrelated set of information.  

A neural network is comprised of layers of "perceptrons" (nodes) that learn from data by adjusting "weights" to detect patterns and reduce errors. Each perceptron calculates a weighted sum of its inputs and applies an activation function to determine its output, which then serves as input for the next layer. 

A single perceptron is a fundamental building block, while a multi-layer perceptron (MLP) is a common neural network structure with multiple interconnected layers.

Please refer to the following for more information:

• Wikipedia: Neural Networks
• Wikipedia: Artificial Neural Networks

- Understanding Artificial Neural Networks (ANNs)

Artificial neural networks (ANNs) are computing systems inspired by the structure and function of the human brain, used for tasks like pattern recognition and image analysis in AI and deep learning (DL). 

These networks consist of interconnected nodes, organized in layers, including input, hidden, and output layers. Deep neural networks have multiple hidden layers, enabling complex tasks through various data pathways. Training these networks involves feeding them with labeled data to learn patterns, and they can be applied to diverse problems, from image classification to medical diagnoses. 

In essence, neural networks provide a powerful framework for building AI systems that can learn from data, recognize patterns, and make predictions, drawing inspiration from the remarkable processing capabilities of the human brain.

1. Key concepts: 

• Nodes/Neurons: The basic units of a neural network, similar to neurons in the brain, that process and transmit information.
• Layers: Groups of interconnected nodes that perform specific tasks, with the input layer receiving data, hidden layers analyzing it, and the output layer producing results.
• Input Layer: The initial layer that receives data into the network.
• Output Layer: The final layer that produces the network's prediction or classification.
• Hidden Layers: Layers between the input and output layers that perform complex computations and feature extraction.
• Deep Neural Networks: Networks with multiple hidden layers, allowing for sophisticated analysis and learning.
• Training: The process of feeding the network with data and corresponding labels to teach it how to recognize patterns.
• Generalization: The ability of a trained network to accurately predict or classify new, unseen data.

2. How Neural Networks Work: 

Neural networks learn by adjusting the connections between neurons (weights) during training. They analyze input data through layers, each performing specific tasks. For example, in image analysis, one layer might detect edges, another might identify colors, and subsequent layers might recognize more complex features, ultimately allowing the network to classify objects like cats. 

3. Applications: 

Neural networks are used in a wide range of applications, including: 

• Image and video analysis: Object detection, facial recognition, medical imaging analysis.
• Natural language processing: Machine translation, text generation, sentiment analysis.
• Recommendation systems: Predicting user preferences.
• Financial modeling: Predicting stock prices, fraud detection.
• Robotics: Control and navigation.

- ANNs and Artificial Neurons

Artificial neural networks (ANNs) are a branch of machine learning (ML) models inspired by the neuronal organization found in the biological neural networks in animal brains.

An ANN is made of connected units or nodes called artificial neurons, which loosely model the neurons in a brain. These are connected by edges, which model the "synapses" in a brain. 

An artificial neuron receives signals from connected neurons, then processes them and sends a signal to other connected neurons. The "signal" is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs, called the activation function. 

Neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. 

Typically, neurons are aggregated into layers. Different layers may perform different transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly passing through multiple intermediate layers (hidden layers). A network is typically called a deep neural network if it has at least 2 hidden layers. 

ANNs are used for predictive modeling, adaptive control, and other applications where they can be trained via a dataset. They are also used to solve problems in AI. ANNs can learn from experience, and can derive conclusions from a complex and seemingly unrelated set of information. 

- ANNs and Their Role in Machine Learning

Artificial Neural Networks (ANNs) are a type of machine learning (ML) algorithm that mimics the structure and function of the human brain. They are designed to recognize patterns, learn from data, and make predictions. ANNs consist of interconnected nodes, or "neurons," that process and transmit information.

• Interconnected Nodes: ANNs consist of layers of interconnected nodes, also called neurons, that process and transmit information.
• Pattern Recognition and Learning: ANNs can recognize complex patterns in data and learn from it to make predictions or decisions.
• Mathematical Models: ANNs are mathematical models that use algorithms to determine the strength of each neuron and its connections to other neurons.
• Training Process: ANNs learn through a process called training, where they adjust their weights and connections based on the difference between their predicted and actual outputs.
• Applications: ANNs are used in various applications, including image recognition, natural language processing, and time series prediction.
• Types of ANNs: There are different types of ANN architectures, such as feedforward networks, recurrent networks, and convolutional networks, each suited for specific tasks.

- The Key Components of an Artificial Neural Network (ANN) 

Artificial Neural Networks (ANNs) typically have three main layers: an input layer, a hidden layer (or multiple hidden layers), and an output layer. The input layer receives the initial data, the hidden layer(s) process the data and extract features, and the output layer produces the final prediction or classification result. 

The number of layers in an ANN can vary depending on the architecture. The depth of an ANN refers to the number of hidden layers.  

An Artificial Neural Network (ANN) is a computational model inspired by the structure and function of the human brain, composed of interconnected layers of artificial neurons or nodes. 

These networks are designed to recognize patterns, learn from data, and make predictions or classifications. ANNs are fundamental to areas like deep learning and AI. 

In essence, ANNs learn by training on a dataset, adjusting the weights and biases of the connections between neurons to minimize the difference between the predicted and actual results, a process often guided by the backpropagation algorithm. This allows the network to identify patterns and make accurate predictions on new data.

The main components of an ANN include: 

1. Input Layer: This layer receives the initial data or input features that the ANN will process. The number of nodes (neurons) in this layer corresponds to the number of input variables or features in the data. 

Function: Receives the raw input data from the outside world.

Example: In image recognition, the input would be the pixel values of an image. 

Key Roles:

• It doesn't perform computations but rather passes the data to the next layer.
• Consists of nodes, each representing a feature of the input data.

2. Hidden Layers:  These layers perform complex computations on the input data, extracting meaningful patterns and features. ANNs can have one or more hidden layers. Each hidden layer analyzes the output from the previous layer, processes it further, and passes it on to the next layer. The number of hidden layers and the number of neurons within each hidden layer are often determined by the complexity of the data and the desired performance of the ANN.

Function: Processes the data received from the input layer and transforms it into more abstract information. 

Key Roles:

• They are the computational workhorse of ANNs, where the actual learning takes place.
• The number of hidden layers and neurons within them determines the network's capacity to learn complex patterns.
• Each hidden layer applies weights and biases to the inputs and passes the result through an activation function, introducing non-linearity to the network.
• Deeper networks with more hidden layers can learn more intricate representations but require more data and computation.
• A network with more than one hidden layer is considered a deep neural network.
• Overfitting is a potential issue where a network learns the training data too well, hindering its performance on new, unseen data. Regularization techniques like dropout are used to combat overfitting and improve generalization.

3. Output Layer: This layer produces the final output or prediction of the ANN. The number of neurons in this layer depends on the type of problem being solved (e.g., classification, regression). For example, in a binary classification task, the output layer would have one neuron, while in a multi-class classification task, it would have multiple neurons.

Function: Produces the final decision or prediction based on the processed data from the previous layers. 

Key Roles: 

The number of neurons depends on the problem type.

• Binary classification (e.g., yes/no): One neuron.
• Multi-class classification: One neuron per class, with activation functions like Softmax.
• Regression: Typically one neuron.

- How an ANN Works

An artificial neural network (ANN) consists of a layer of nodes, which contains an input layer, one or more hidden layers, and an output layer. 

Each node or artificial neuron is connected to another node and has associated weights and thresholds. If the output of any single node is above a specified threshold, that node is activated, sending the data to the next layer of the network. Otherwise, no data is passed to the next layer of the network.

Neural networks rely on training data to learn and improve their accuracy over time. But once these learning algorithms are fine-tuned to improve accuracy, they become powerful tools in computer science and AI, allowing us to classify and cluster data at high speed. 

Speech recognition or image recognition tasks can take minutes instead of hours compared to manual recognition by human experts. One of the most famous neural networks is Google's search algorithm.

1. How a Perceptron Works:

• Inputs and Weights: A perceptron receives input data, and each input is multiplied by a corresponding "weight".
• Weighted Sum: The perceptron calculates the sum of these weighted inputs.
• Activation Function: An activation function processes this sum, determining whether the perceptron "fires" (activates) or not.
• Output: The output of this activation function then serves as the input for the next layer of neurons in the network.

2. The Role of Layers in Neural Networks:

• Input Layer: The first layer that receives raw input data.
• Hidden Layers: One or more layers between the input and output layers. These layers process the data in stages to detect complex features and patterns, with early layers often identifying simple features and later layers combining them into more abstract concepts.
• Output Layer: The final layer that produces the network's prediction or result.

3. Learning and Error Reduction:

• Learning from Data: Neural networks learn by processing training data.
• Adjusting Weights: The network attempts to minimize the "cost error" or discrepancy between its predicted output and the actual correct output.
• Backpropagation: During training, errors are propagated backward through the network to adjust the weights, making the network more accurate over time.

- Neural Networks in Deep Learning (DL)

Neural networks are the core machinery that make DL so powerful. Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated. 

A Neural Network is a computer system designed to work by classifying information in the same way a human brain does. It can be taught to recognize, for example, images, and classify them according to elements they contain. The development of neural network has been key to teaching computers to think and understand the world in the way we do, while retaining the innate advantages they hold over us such as speed, accuracy and lack of bias. 

Neural networks help us cluster and classify. You can think of them as a clustering and classification layer on top of the data you store and manage. They help to group unlabeled data according to similarities among the example inputs, and they classify data when they have a labeled dataset to train on. 

Neural networks can also extract features that are fed to other algorithms for clustering and classification; so you can think of deep neural networks as components of larger machine-learning applications involving algorithms for reinforcement learning, classification and regression.

- Deep Learning Algorithms and ANNs

Deep learning algorithms such as ANNs, are able to processing, technical and fundamental information from news, in order to detect features and patterns that can affect the pricing behavior in financial markets. The power of deep learning lies in the fact that can manage big amounts of data, and simultaneously, ANNs can learn from financial data and understand the price movements in stock markets, in a matter of seconds. 

An Artificial Intelligence System (AIS), can suggest the optimal solutions and by minimize the risk in trading strategies, can produce predictions that will be calculated based on the information we have gained, and in combination with other intelligent methods such as natural language processing (NLP), much more accurate results could be produced. 

Hybrid deep learning systems have been widely used in financial services, by detecting new market trends and by offering significant profits to investors, by forecasting the prices. All this without the need to apply a new investment theory as long as deep learning algorithms will discover in the data, structures that have never been studied or explained in the past. 

- Perceptrons - How Neural Networks Work

A normal neural network consists of multiple layers called input, output, and hidden layers. In each layer, each node (neuron) is connected to all nodes (neurons) in the next layer through parameters called "weights". 

A neural network consists of nodes called "perceptrons" that perform the necessary computations and detect the "characteristics" of the neural network. These perceptrons try to reduce the final cost error by adjusting the weight parameters. Furthermore, the perceptron can be thought of as a single-layer neural network. 

When considering a perceptron, its work can be described as follows: When you feed data with random weights into the model, it generates a weighted sum of them. Based on this value, the activation function determines the activation state of the neuron. The output of this perceptron can be used as the input to the next neuron layer. 

- Training a Neural Network

In simple terms, what we usually do when training a neural network is to calculate the loss (error value) of the model and check if it decreases. 

If the error is higher than expected, we have to update model parameters such as weights and bias values. Once the loss is below the expected error bound, we can use the model.

The perceptron is activated when there is a satisfying input. 

• Initially, the dataset should be fed into the input layer and then flow to the hidden layer.
• The connections that exist between the two layers randomly assign weights to the inputs.
• Add a bias to each input. Bias is the constant used in the model to best fit the given data.
• The weighted sum of all inputs will be sent to a function that decides the neuron's activity state by computing the weighted sum and adding a bias. This function is called the activation function.
• The node that needs to be triggered for feature extraction is determined according to the output value of the activation function.
• The final output of the network is then compared to the labeled data required for our dataset to calculate the final cost error. The cost error actually tells us how "bad" our network is. Therefore, we want the error to be as small as possible.
• Adjust the weights by backpropagation, thereby reducing the error. This back-propagation process can be thought of as the central mechanism of neural network learning. It basically fine-tunes the weights of the deep neural network to reduce the cost value.

- Mutilayer Perceptrons - Deep Neural Networks

Multilayer Perceptrons (MLPs) are a class of feedforward artificial neural networks (ANNs), often referred to as Deep Neural Networks (DNNs) due to their multiple layers. 

The training process of an MLP involves several key steps: 

• Input and Forward Pass: The dataset is fed into the input layer and propagates through subsequent hidden layers to the output layer.
• Weighted Sum and Bias: Connections between layers have assigned weights, initially random. A bias term, a constant value, is added to each input's weighted sum.
• Activation Function: The weighted sum of inputs plus the bias is passed through an activation function. This function determines the neuron's activity state and its output value, which in turn influences which nodes are triggered for feature extraction.
• Cost Error Calculation: The network's final output is compared to the desired labeled data to calculate the cost error. This error quantifies how inaccurate the network's predictions are, with the goal being to minimize it.
• Backpropagation and Weight Adjustment: If the cost error is too high, the backpropagation algorithm is used to adjust the weights and biases of the network. This process effectively fine-tunes the network's parameters to reduce the error.
• Iteration and Convergence: The training process iterates through these steps, continuously adjusting weights and biases, until the cost error falls below a predefined acceptable threshold. Once the error is sufficiently low, the trained model can be used for predictions on new, unseen data.