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Accern accelerates AI workflows for financial service enterprises with a no-code AI platform.

The best data teams from the world's leading organizations, such as Allianz, IBM, and Jefferies, are using Accern to build and deploy AI use cases powered by our adaptive NLP and forecasting features.

11/04/2021

Future Here!

Neural network, a computer program that operates in a manner inspired by the natural neural network in the brain. The ob...
11/04/2021

Neural network, a computer program that operates in a manner inspired by the natural neural network in the brain. The objective of such artificial neural networks is to perform such cognitive functions as problem solving and machine learning. The theoretical basis of neural networks was developed in 1943 by the neurophysiologist Warren McCulloch of the University of Illinois and the mathematician Walter Pitts of the University of Chicago. In 1954 Belmont Farley and Wesley Clark of the Massachusetts Institute of Technology succeeded in running the first simple neural network. The primary appeal of neural networks is their ability to emulate the brain’s pattern-recognition skills. Among commercial applications of this ability, neural networks have been used to make investment decisions, recognize handwriting, and even detect bombs.

Basics of Neural NetworksNeural networks, in the world of finance, assist in the development of such process as time-ser...
11/04/2021

Basics of Neural Networks
Neural networks, in the world of finance, assist in the development of such process as time-series forecasting, algorithmic trading, securities classification, credit risk modeling and constructing proprietary indicators and price derivatives.
A neural network works similarly to the human brain’s neural network. A “neuron” in a neural network is a mathematical function that collects and classifies information according to a specific architecture. The network bears a strong resemblance to statistical methods such as curve fitting and regression analysis.
A neural network contains layers of interconnected nodes. Each node is a perceptron and is similar to a multiple linear regression. The perceptron feeds the signal produced by a multiple linear regression into an activation function that may be nonlinear.

Why are neural networks important?Neural networks are also ideally suited to help people solve complex problems in real-...
11/04/2021

Why are neural networks important?
Neural networks are also ideally suited to help people solve complex problems in real-life situations. They can learn and model the relationships between inputs and outputs that are nonlinear and complex; make generalizations and inferences; reveal hidden relationships, patterns and predictions; and model highly volatile data (such as financial time series data) and variances needed to predict rare events (such as fraud detection). As a result, neural networks can improve decision processes in areas such as:
Credit card and Medicare fraud detection.
Optimization of logistics for transportation networks.
Character and voice recognition, also known as natural language processing.
Medical and disease diagnosis.
Targeted marketing.
Financial predictions for stock prices, currency, options, futures, bankruptcy and bond ratings.
Robotic control systems.
Electrical load and energy demand forecasting.
Process and quality control.
Chemical compound identification.
Ecosystem evaluation.
Computer vision to interpret raw photos and videos (for example, in medical imaging and robotics and facial recognition).

What is a Neural Network?A neural network is a series of algorithms that endeavors to recognize underlying relationships...
11/04/2021

What is a Neural Network?
A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature. Neural networks can adapt to changing input; so the network generates the best possible result without needing to redesign the output criteria. The concept of neural networks, which has its roots in artificial intelligence, is swiftly gaining popularity in the development of trading systems.

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