Neural Networks

Neural Networks

1. Real-world business applications for neural networks are booming. In some cases, NNs have already become the method of choice for businesses that use hedge fund analytics, marketing segmentation, and fraud detection.

   • Banking: Credit card attrition, credit and loan application evaluation, fraud and risk evaluation, and loan delinquencies
   • Business Analytics: Customer behavior modeling, customer segmentation, fraud propensity, market research, market mix, market structure, and models for attrition, default, purchase, and renewals
   • Defense: Counterterrorism, facial recognition, feature extraction, noise suppression, object discrimination, sensors, sonar, radar and image signal processing, signal/image identification, target tracking, and weapon steering
   • Education: Adaptive learning software, dynamic forecasting, education system analysis and forecasting, student performance modeling, and personality profiling
   • Financial: Corporate bond ratings, corporate financial analysis, credit line use analysis, currency price prediction, loan advising, mortgage screening, real estate appraisal, and portfolio trading
   • Medical: Cancer cell analysis, ECG and EEG analysis, emergency room test advisement, expense reduction and quality improvement for hospital systems, transplant process optimization, and prosthesis design
   • Securities: Automatic bond rating, market analysis, and stock trading advisory systems
   • Transportation: Routing systems, truck brake diagnosis systems, and vehicle scheduling

2. Neural vs. Conventional Computers: Here are some of the other major differences between conventional and neural computers:

   • Following Instructions vs. Learning Capability: Conventional computers learn only by performing steps or sequences set by an algorithm, while neural networks continuously adapt their programming and essentially program themselves to find solutions.
   • Rules vs. Concepts and Imagery: Conventional computers operate through logic functions based on a given set of rules and calculations. In contrast, artificial neural networks can run through logic functions and use abstract concepts, graphics, and photographs. Traditional computers are rules-based, while artificial neural networks perform tasks and then learn from them.
   • Complementary, Not Equal: Conventional algorithmic computers and neural networks complement each other. Some tasks are more arithmetically based and don’t require the learning ability of neural networks. Often though, tasks require the capabilities of both systems. In these cases, the conventional computer supervises the neural network for higher speed and efficiency.

3. Challenges of Neural Networks: Neural networks are where most advances are being made right now. Things that were impossible only a year or two ago regarding content quality are now a reality.” As useful as neural networks can be, challenges in the field abound:

   • Training: A common criticism of neural networks, particularly in robotics applications, is that excessive training for real-world operations is mandatory.
   • Theoretical Issues: Unsolved problems remain, even for the most sophisticated neural networks.
   • Inauthenticity: The theoretical challenges we address above arise because neural networks don’t function exactly as human brains do — they operate merely as a simulacrum of the human brain. The specifics of how mammalian neurons code information is still an unknown. Artificial neural networks don’t strictly replicate neural function, but rather use biological neural networks as their inspiration.
   • Hardware Issues: This century’s focus on neural networks is due to the million-fold increase in computing power since 1991. More hardware capacity has enabled greater multi-layering and subsequent deep learning, and the use of parallel graphics processing units (GPUs) now reduces training times from months to days.

4. A branch of machine learning, neural networks (NN), also known as artificial neural networks (ANN), are computational models — essentially algorithms. Neural networks have a unique ability to extract meaning from imprecise or complex data to find patterns and detect trends that are too convoluted for the human brain or for other computer techniques.

   • Neural Networks possess the following powerful attributes:
     • Adaptive Learning: Like humans, neural networks model non-linear and complex relationships and build on previous knowledge.
     • Self-Organization: The ability to cluster and classify vast amounts of data makes neural networks uniquely suited for organizing the complicated visual problems posed by medical image analysis.
     • Real-Time Operation: Neural networks can (sometimes) provide real-time answers, as is the case with self-driving cars and drone navigation.
     • Prognosis: NN’s ability to predict based on models has a wide range of applications, including for weather and traffic.
     • Fault Tolerance: When significant parts of a network are lost or missing, neural networks can fill in the blanks. This ability is especially useful in space exploration, where the failure of electronic devices is always a possibility.
   • Neural networks are highly valuable because they can carry out tasks to make sense of data while retaining all their other attributes. Here are the critical tasks that neural networks perform:
     • Classification: NNs organize patterns or datasets into predefined classes.
     • Prediction: They produce the expected output from given input.
     • Clustering: They identify a unique feature of the data and classify it without any knowledge of prior data.
     • Associating: You can train neural networks to "remember" patterns. When you show an unfamiliar version of a pattern, the network associates it with the most comparable version in its memory and reverts to the latter.

5. Bibliografía: Aplicaciones comerciales y de la vida real de las redes neuronales. (2018). Aplicaciones comerciales y de la vida real de las redes neuronales. https://acortar.link/B1XGvg