Neural Networks became key in the Machine Learning (ML) subfields' world as they emulate the neuronal structure of the human brain. This Deep Learning (DL) element training relies on data to understand and comprehend human patterns and thought processes. NN's goal is to use previous knowledge and data to make accurate predictions. It can be complicated to understand, but today is your lucky day. You'll learn how Neural Networks work, their types, history, features, advantages, and disadvantages, and how to train this sub-field of ML. Let’s read on!
What are Neural Networks?
A Neural Network comprises a series of algorithms. These algorithms focus on recognizing the underlying relationships in a data set to imitate the cognitive process of the human brain. Neural Networks are neuron systems capable of modifying inputs to achieve the goal of generating the best possible outcome without redesigning the output criteria. In sum, a Neural Network learns from data in the same way humans learn from experience!
Within Neural Networks, a neuron is a math function that processes the input to deliver an outcome, allowing users to classify information according to some specific architecture. It’s similar to statistical methods such as curve fitting and regression analysis. NNs contain layers of interconnected nodes, and each node is known as a perceptron, similar to multiple linear regression. In this case, the perceptron can introduce a network that performs numerous linear transformations, as in linear regression within an activation function that introduces nonlinearities to the output.
History of Neural Networks
Neural Networks have a rich and interesting history that dates back to the 1940s. The human brain's structure and functionality inspired the creation of this subset of Machine Learning. However, NN remained a theoretical model with limited practical applications during the first stage. But that's not all; during the 80s and 90s, the experts moved the development of algorithms to a new, more efficient phase and greater power from a computational model point of view.
Specifically, in 1943, Walter Pitts and Warren McCulloch of the University of Illinois and the University of Chicago published "A Logical Calculation of the Ideas Immanent in Nervous Activity." This investigation analyzed how the human brain could produce complex patterns to the point of being simplified to a binary logical structure with only true/false connections. Subsequently, the lead role in the Percepton's development fell to Frank Rosenblatt of the Cornell Aeronautical Laboratory in 1958. Practically, he introduced weights to McCulloch's and Pitt's work and leveraged it to demonstrate how a computer could use Neural Network models to detect images and make inferences about them.
Some years later, there was a financial drought, and the work in progress reached a standstill in the 1970s. However, this was no reason to stop the thirst for innovation and discovery. Then, in 1982, Jon Hopfield presented Hopfield Net, an article about recurrent Neural Networks. In this same sequence, the concept of the backpropagation method resurfaced, and many researchers began to understand its potential for Neural Networks. Furthermore, Paul Werbos was one of the contributors to this study and is credited with the main contribution during this time in his doctoral thesis.
This continuous process allows the application of Neural Networks in many fields, including pattern recognition, image processing, Natural Language Processing (NLP), and Machine Learning (ML). For this reason, it applies in various industries for tasks such as voice recognition, computer vision, predictive modeling, and other uses like discovering new medications, identifying trends within the financial market, and performing massive scientific calculations.
How Does Neural Networks Work?
The human brain is the principal inspiration for the Neural Network's architecture. In this case, the experts have converted the human brain model into an AI model that serves many things. Neural Networks contemplate artificial neurons that work together to solve problems. These artificial neurons are software modules known as nodes, and Artificial Neural Networks are software programs or algorithms that use software systems to solve mathematical problems. To understand it, you have to start by knowing the architecture of Neural Networks.
The principal function of a Neural Network is to transform input into meaningful output. A Neural Network includes two main layers, specifically the input and output. It also contains one or more hidden relationships between both principal layers. Inside the architecture of the Neural Network, neurons converge between them, and hence, all neurons share the same connection. The network can recognize and observe each aspect of the available data set and how different parts of the data may or may not relate to each other. Plus, Neural Networks can find complex patterns in large volumes of data.
To be more specific, the basic architecture of a Neural Network has interconnected artificial neurons in three layers. These layers are:
1. Neural Networks Input Layer: Information from the outside world enters the artificial neural network from the input layer, where the input nodes must process the data, classify it, and then pass it to the next layer.
2. Neural Networks Hidden Layer: These layers have the work to take their input using input layers or the other hidden layers. In a manner, artificial neural networks can have many hidden layers. In this same form, each hidden layer analyzes the output of each before layer; it's processed with greater emphasis and then sent to the next layer.
3. Neural Networks Output Layer: It's the end layer capable of providing the final result that involves the entire data processing that realizes the artificial Neural Network, and it can have one or various nodes. For example, if you have a binary classification problem (yes/no), the output layer will have an output node whose result will be 1 or 0. In contrast, if you have a multiclass classification problem, the output layer can conform to more than one output node.
It's necessary to clarify that each node integrates a connection with other nodes corresponding to another layer, and each bond generated has a specific weight. It means that each neuron has a particular connection weight according to importance compared with other inputs. After that, when all the values of the input layer nodes are multiplied (along with their weight) and summed up, a new value corresponding to the hidden layer is generated. This process is known as a linear transform. The hidden layer’s outputs have a predefined "activation" function capable of determining whether this node will become "active" or not and how "active" it might be; this process allows the network to learn nonlinearities from data.
Imagine you are going to make coffee. The neurons, in this case, will be the water. Coffee, milk, and other ingredients, like caramel, form part of the starting point. Then, following the indications, the amount of each element represents the "Weight." You can add the sugar and caramel once you put the coffee in the coffee maker with the water. Then, all the ingredients will mix and take another form. This process of transformation represents the neurons with its "Activation functions." Finally, the components represent the hidden layer, and the heating means the activation process that produces the result: Coffee.
Types of Neural Networks
There are various Neural Network types according to how data flows from the input node to the output node. In the next section, you can observe the main types of this sub-field of Machine Learning.
1. Fully Connected Neural Networks
A fully connected neural network comprises several fully combined layers connecting every neuron in a single layer to every neuron in the next. The main advantage of fully connected networks is that they are "structure agnostic," meaning no specific input assumptions are required. While structure agnostic makes fully connected networks very broadly applicable, they perform worse than special-purpose networks customized to a problem area's structure.
2. Recurrent Neural Networks
The next one is more complex, capable of saving the output from the processing nodes and feeding the results into the model. Likewise, it's the way this model learns to predict the layer's outcome. Each node corresponds to Recurrent Neural Networks that work as memory cells, calculating and operating implementation. These models correspond to Deep Learning algorithms, often containing various hidden layers.
Compared to the Neural Networks type mentioned above, both types share the same frontal spread. Despite this, Recurrent Neural Networks remember each part of the processed information to reuse it later. If the network makes incorrect predictions, the system automatically learns and follows work to achieve the right prediction during the backpropagation phase. This type can be useful in circumstances such as text-to-speech software.
3. Convolutional Neural Networks
It's one of the most popular models today. This model uses a variation of multilayer perceptrons, specifically a simple model corresponding to a biological neuron in an Artificial Neural Network. It contains one or more convolutional layers that can fully connect or group. Convolutional layers can create feature maps and register a region of a much larger image normally divided into rectangles. In addition, their networks lie in image recognition in most advanced Artificial Intelligence applications. Their common uses include facial recognition, signal processing, and image classification.
4. Deconvolutional Neural Networks
Deconvolutional Neural Networks simply operate in the opposite direction of Convolutional Neural Networks. The network's application is to detect items that might have been identified as important by a Convolutional Neural Network, considering that these elements probably went through a discarding process during the execution of the Convolutional Neural Network. This Neural Network approach is present in image analysis and object detection.
5. Modular Neural Networks
In drug discovery, ANNs expedite the identification of potential drug candidates and predict their efficacy and safety, significantly reducing development time and costs. Additionally, their application in personalized medicine and healthcare data analysis leads to more tailored therapies and efficient patient care management. Modular Neural Networks contain several networks that work independently of each other and don't interact between them during the analysis process. Conversely, these processes aim to make complex and elaborate computer processes more efficient. Considering other modular industries, such as modular real estate, the goal of network independence is for each module to be responsible for a specific part of a larger picture.
6. Transformer Neural Networks
Transformers are a type of neural network architecture that is becoming increasingly popular. OpenAI recently employed transformers in their language models and DeepMind in AlphaStar, a program that defeated a top professional Starcraft player. Transformers are essential for tackling the issue of sequence transduction, also known as neural machine translation. It includes any task that converts an input sequence to an output sequence and contains elements such as speech recognition, text-to-speech conversion, etc.
Deep Learning vs Neural Networks
Comparing both terms is among the most popular forms of confusion regarding AI and its subfields. Both concepts are often used interchangeably within the same idea, which can be confusing. First, you must know that "Deep" in Deep Learning (DL) is the depth of layers in a neural network. Even so, some Neural Networks are also often called Deep Neural Networks. In contrast, a Neural Network is a concept that contains three layers (Input, Hidden, and Output) that, in a few words, can be considered a Deep Learning network. To be more specific, a Neural Network with solely two or three layers is a primary Neural Network.
How To Train Neural Networks?
Maybe it's the part that is more tempting for you and the most relevant in this blog post. At first, understanding how the training works can be difficult, but what better way to understand it than with a good example? Let us begin.
Training an artificial Neural Network implies large amounts of data. From the simplest perspective, this training uses data to provide information and tell the network the desired outcome. For example, suppose you want to build a network capable of identifying bird species. In that case, the initial training must be rooted in images of birds and animals that are not birds, including the integration of airplanes and any flying object. Each input would have a companion, specifically a matching identification, such as the name of a specific bird, or additional data, such as "not bird" or "not animal." The responses should allow you to achieve an adjustable model according to its internal weights to learn how to guess the right bird species with the greatest accuracy.
Specifically, imagine that the node layers Alpha, Beta, and Gamma tell the note Delta that the current image input corresponds to a Hawk, but node Epsilon says it's a Condor. You'd say that the training program confirms that it is, in fact, a Hawk. After this, the Delta node will reduce the relevance it gives to the Epsilon input while increasing the importance you place on Alpha, Beta, and Gamma data.
Afterward, follow these rules and make determinations, specifically each node's decision about what to send to the next level based on input patterns from the previous level; it's important to clarify that Neural Networks use several principles. These principles include gradient-based training, fuzzy logic, Bayesian methods, and genetic algorithms.
Where to Use Neural Networks?
It contemplates various uses, with applications in financial operations, business planning, trading, business analysis, product maintenance, etc. It has obtained a generalized adoption in business applications such as market forecasting and research solutions, fraud detection, and risk assessment. Following the uses line, an NN can evaluate data prices and discover opportunities to take advantage of this, making business decisions based on data analysis.
Neural networks in finance can process hundreds of thousands of bits of transaction data, being beneficial to the understanding of trading volume, trading range, and asset correlation or setting volatility expectations for specific investments. Due to a human not efficiently sifting through years of data (sometimes collected at second intervals), Neural Networks interfere with this process and help to detect trends, analyze outcomes, and forecast future asset class value movements.
They are present everywhere, from medical diagnosis via image classification, financial forecasting using historical data of financial instruments, marketing through social network filtering to behavioral data analysis, process and control quality, identification of chemical compounds, etc. We can also include artificial vision, voice recognition, Natural Language Processing (NLP), sentiment analysis, recommendation engines, etc.
Neural Networks Pros and Cons
In a world full of advantages and disadvantages, Neural Networks does not go unnoticed. First, starting with the benefits points, it is viable, considering that neural networks can work continuously and be more efficient than humans or simple analytics models. Also, they can go through a programming process to understand and learn using previous data and determine future results based on similarity at earlier inputs.
Neural Networks that leverage online cloud services have the potential to mitigate the risks compared to traditional systems that depend on local technology hardware. These networks can often make multiple tasks simultaneously or distribute the tasks for modular networks to run simultaneously. Ultimately, we can mention that neural networks are going through a process of continuous expansion toward new applications, being useful in industries such as medicine, science, finance, security, development, etc.
Now it's time to talk about the things that are not so pink; considering that neural networks still depend on online platforms in the first instance, they require a hardware component to create a neural network. For this reason, this situation implies a physical risk that depends on the complex systems, configuration requirements, and possible physical maintenance.
Although the Neural Network's complexity is a strength, developing a specific algorithm for a particular task can take months (if not more). Consequently, detecting errors or deficiencies in the process can be complex, specifically if outcomes are estimates or theoretical ranges.
In the ultimate instance, we can mention that they can be complex to audit, considering that some Neural Networks processes may seem like a black box where inputs pass, networks realize the complicated process, and then the output goes through a notification process. Furthermore, it can also be complicated for professionals to analyze weaknesses within the network's computation or learning process if the network lacks overall transparency about how a model learns about previous activities.
Final Thoughts
Finally, Neural Networks are integrated and complex systems that substantially advance Artificial Intelligence (AI). They have the potential to transform industries ranging from healthcare to banking by producing more accurate, faster, and deeper forecasts to improve the decision-making process and automate jobs. However, they are not without problems, such as the need for large amounts of data and computing power and the difficulty in understanding their complicated operation. Despite these obstacles, their advantages make them an intriguing field of study and application, considering the types of NN and the basic elements that make them up. As technology advances, we should expect to see even more creative use cases with an integration level more relevant to our daily lives.
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