ARTIFICIAL NEURAL NETWORK IN DEEP LEARNING/PYTHON/ARTIFICIAL INTELLIGENCE

 Artificial Neural Network

• Architecture Component
• Different Types of Artificial Neural Networks
• Applications of Artificial Neural Networks
• Advantages of Artificial Neural Networks
• Disadvantages of Artificial Neural Networks

Artificial Neural Networks (ANNs) are made to replicate how the human brain works, aiming to simulate its functions. They constitute a subfield of artificial intelligence inspired by biological neural networks. Similar to how neurons are connected in the human brain, artificial neuron networks consist of units connected across multiple layers.

Artificial neurons, often referred to as units, are arranged in layers to form the network structure of an ANN. Depending on how complicated the network is, a layer may have a few hundred or millions of units in it. An artificial neural network AI typically consists of intermediate hidden layers, an output layer, and an input layer. External data is fed into the input layer so that the neural network in artificial intelligence may analyze or learn from it. After that, this data is transformed by processing it through one or many other hidden layers. The output layer receives the changed data at this point and provides the network's reaction to the input data.

Interconnections between most artificial neural networks in machine learning units incorporate weights, dictating the impact of one unit on another. As information moves between units, the artificial neural network algorithm accumulates knowledge about the data, leading to an output from the output layer.

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What is Artificial Neural Network?

The term “Artificial Neural Network” is derived from the biological neural network that is present in the human brain. Neuron AI in artificial neuron networks has another more common name rather than units which is node most commonly we refer to the node for the neurons.

Belo is the typical image of a human neuron.

In artificial neural networks (ANNs), the structure mirrors the structure of biological neural networks (NNs). In ANNs, inputs correspond to dendrites, nodes represent cell nuclei, weights are analogous to synapses, and outputs resemble axons.

This table illustrates the parallelism between biological and artificial neural networks.

Biological Neural Network	Artificial Neural Network
Dendrites	Inputs
Cell nucleus	Nodes
Synapse	Weights
Axon	Output

The artificial neural network and AI emulates interconnected brain cells and is engineered by programmers. The human brain has approximately 100 billion neurons, and in this, each neuron has connections, ranging from 1,000 to 100,000 associations. An effective way to comprehend an artificial neural network example is by considering a digital gate, which accepts an input and produces an output. 

Real-World Example for Artificial Neural Networks

Let’s look at an example, in a big city there is a financial district, that is grappling with a surge in fraudulent transactions. In this financial district, banks were inundated with cases of identity theft and unauthorized access to accounts, causing financial losses and eroding customer trust.

To tackle this situation data scientist Maya turned to artificial neural networks (ANNs). She gathered vast amounts of transactional data, including user behavior, transaction history, and account details.

Using this data, she trained an artificial neural network machine learning model that can detect patterns indicative of fraudulent activity. The network analyzed each transaction in real-time, flagging suspicious behavior such as unusually large transactions, irregular spending patterns, or multiple failed login attempts.

As the ANN model processed more data, its accuracy increased, enabling it to distinguish between legitimate transactions and fraudulent ones with greater precision. This new development ability allowed banks to identify and prevent fraudulent activity before it could inflict financial harm.

How do Artificial Neural Networks learn?

The artificial neural network Python undergoes training using a training dataset. Consider teaching an ANN to recognize a dog: it's exposed to numerous dog images to learn the identification process. Once the training concludes, the network's ability to correctly identify dog images is tested. By presenting new images, the ANN determines whether they depict dogs or not. Human-provided descriptions verify the network's accuracy. If discrepancies arise, backpropagation comes into play. This method involves specifying the weights of the links in the units of the artificial neural network (ANN) and adjusting them according to the error rate. This iterative process continues until the network can effectively identify dogs from the images, minimizing errors.

The architecture of an artificial neural network

To grasp the workings of a neural network, understanding its components is crucial. A neural network comprises numerous artificial neurons, referred to as units, arranged in layers. Let's explore the different types of layers present in every artificial neural network. The diagram below illustrates the various layers within the network.

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Input Layer: This layer, as implied, receives inputs in various formats provided by users or programmers.

Hidden Layer: Positioned middle of the input and output layers, the hidden layer undertakes calculations crucial for identifying hidden features and patterns within the dataset or training data.

Output Layer: As the name suggests, this layer acts as an endpoint to present the final output to the users. After a series of transformations enabled by the hidden layer, the input is processed, resulting in the result passed through this layer.

The artificial neural network processes inputs by computing the total weighted sum of inputs along with a bias, represented through a transfer function.

This component calculates the weighted sum, which is then passed to the activation function to generate the output. The activation function specifies whether the node should be activated or not. Only activated nodes persist to the output layer. We have many activation functions, therefore we need to carefully choose the most suitable function.

Different types of Artificial Neural Networks

Feedforward Neural Network: It is one of the most common neural networks it operates in a single direction, in this the data moves from the input to the middle layers and from the middle layers to the output layer it does not have backpropagation.

Convolutional NeuralNetwork (CNN): Similar to the feedforward neural network, the CNN incorporates weighted connections between units or nodes, determining the influence of one unit on another. It employs one or multiple convolutional layers that execute convolutional operations on input data and pass the obtained results to subsequent layers. CNNs are extensively utilized in tasks like image processing and speech recognition, particularly in computer vision applications.

Modular Neural Network: Comprising multiple independent neural networks, a modular neural network operates distinctively, with no interaction among its components. Each network handles a specific sub-task with unique inputs. The advantage lies in its modular approach, breaking down the complex computational processes into little components, and reducing complexity while obtaining the desired output.

Radial Basis Function Neural Network: This network takes advantage of the point-to-center distance and uses only two layers. The first layer maps the radial basis functions in the hidden layer, while the output layer computes the resulting output. These networks are typically used in models that represent underlying patterns or features in data sets.

Recurrent Neural Networks(RNNs): RNNs differ by retaining the output of a layer and feeding it back to the input to enhance outcome prediction. It begins similarly to a feedforward neural network, with each node or unit in subsequent layers remembering information from previous steps, functioning akin to a memory cell to improve computational performance.

We will look at these neural networks in much detail in further chapters.

Applications of Artificial Neural Networks

• Social Media: Artificial neural networks play an important role in many social media platforms, on Facebook you might see suggestions for "people you may know", Facebook does this by analyzing user profiles, interests, existing connections, and more to propose potential acquaintances. Facial recognition is another key application, leveraging convolutional neural networks by identifying facial reference points to match them with database records.
• Marketing and Sales: E-commerce platforms like Amazon or Flipkart employ machine learning to recommend a product to the user based on their browsing history. This personalized marketing spans various sectors like food services, hospitality, movies, and books. Artificial neural networks discern customer preferences, shopping history, and dislikes to tailor marketing strategies accordingly.
• Healthcare: Artificial neural networks find extensive use in healthcare, particularly in cancer detection. In oncology, they train algorithms to identify cancerous tissues at microscopic levels with accuracy akin to trained physicians. Additionally, we can use facial analysis using photos to aid in identifying rare diseases in their early stages, which increases the doctor's diagnostic capabilities, and helps the medical sector globally.
• Personal Assistants: Nowadays we often use digital personal assistants like Siri and Alexa they heavily rely on speech recognition and Natural Language Processing (NLP) which in its core uses artificial neural networks. NLP manages language syntax, semantics, speech accuracy, and ongoing conversations, enabling assistants to interact with users effectively.

Advantages of Artificial Neural Networks

• Flexibility to Complex Patterns: Complex patterns that would be challenging for traditional algorithms to detect and understand are among the patterns in data that ANNs specialize in recognizing.
• Learning Capabilities: They can pick up knowledge and get better with experience. ANNs improve their decision-making and forecast accuracy by iterations and changes to its internal parameters (weights).
• Parallel processing is the ability of Artificial Neural Networks (ANN) to carry out operations concurrently across many neurons or nodes. They may handle large volumes of data and carry out intricate computations more quickly because to this function.
• Extrapolating learnt patterns, artificial neural networks (ANNs) may categorize or forecast based on unknown or unseen input. Overfitting is lessened and model performance for new cases is enhanced by this generalizability.
• Feature Extraction: Artificial Neural Networks (ANNs) may independently extract significant features from raw data in some models, like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), therefore minimizing the requirement for human feature design.
• Fault Tolerance: Because ANNs are dispersed and have redundancy in information representation, they may frequently put up with mistakes or missing data without much sacrificing their overall performance.
• Applications of their versatility include time series analysis, natural language processing, picture and audio recognition, and recommendation systems. Their adaptability highlights their efficacy for a range of issues.
• Performance and accuracy of ANNs can be increased over time by their ability to continually store and adapt to new data as it becomes available.

Disadvantages of Artificial Neural Networks

• Computational complexity: Training big neural networks with several layers and neurons takes a lot of processing power, which increases time and energy use.
• Large datasets are necessary for ANNs to be trained to provide good generalizations. Little data might cause overfitting or inadequate generalization.
• Overfitting: Particularly with noisy or little datasets, complex neural network designs are prone to overfit. This problem is lessened by regularization methods or dropout layers.
• Hyperparameter sensitivity: A lot of hyperparameters (learning rate, network architecture, activation functions) in ANNs require fine-tuning. Appropriate value selection can be difficult and have a big effect on model performance.
• Interpretability: Because ANNs are black-box systems, it might be challenging to figure out how they make their judgments. Particularly in fields where openness is essential, this lack of interpretability might be problematic.
• Deep neural network training may be laborious, especially on big datasets, occasionally needing a lot of time and computing power.
• Adversarial Attack Vulnerability: Adversarial attacks can cause false predictions from even minute and undetectable changes to input data.
• Data Dependency: ANNs mostly depend on the calibre and representativeness of the data they are trained on. Predictions from biased or unrepresentative models may be erroneous.
• gear Dependency: Complex neural networks may not be as accessible or practical for some applications or settings if they need specialised gear to develop and train.

Summary

With its linked nodes arranged into layers that process information, artificial neural networks (ANNs) resemble the brain's neural architecture. Through methods like backpropagation, these networks use iterative adjustments of internal parameters to learn from data. ANNs are excellent for classifying and regressing in a variety of fields including finance, natural language processing, and picture identification. Because of their flexibility and ability to manage complex tasks, they continue to be essential in advancing machine learning, robotics, and artificial intelligence (AI) even if they come with difficulties including computational complexity, data dependency, and interpretability problems.