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Artificial Neural Networks and Neural Networks Applications
Artificial Neural Networks are the computational models inspired by the human brain.
Artificial Neural Networks are the biologically inspired simulations performed on the computer to perform certain specific tasks like –
The term ‘Neural’ is derived from the human (animal) nervous system’s basic functional unit ‘neuron’ or nerve cells which are present in the brain and other parts of the human (animal) body.
Neural network is a group of algorithms that certify underlying relationship in a set of data similar like human brain.
Neural network helps to change input so that network gives best result without redesigning the output procedure.
Artificial Neural Networks can be viewed as weighted directed graphs in which artificial neurons are nodes, and directed edges with weights are connections between neuron outputs and neuron inputs.
Weights are the information used by the neural network to solve a problem. Typically weight represents the strength of the interconnection between neurons inside the Neural Network.
In case the weighted sum is zero, bias is added to make the output not- zero or to scale up the system response.
Some of the commonly used activation function is – binary, sigmoidal (linear) and tan hyperbolic sigmoidal functions(nonlinear).
In this type of Artificial Neural Network, electrically adjustable resistance material is used to emulate the function of synapse instead of software simulations performed in the neural network.
When Neural Network Algorithms developed to the point where useful things can be done with 1000’s of neurons and 10000’s of synapses, high-performance Neural Networks hardware will become essential for practical operation.
The performance of implementation is measured by connection per the second number (cps), i.e., the number of the data chunk is transported through the edges of the neural network.
While the performance of the learning algorithm is measured in the connection updates per second (cups) The neural network learns by adjusting its weights and bias (threshold) iteratively to yield desired output.
Here, after finding an error (the difference between desired and target), the error is propagated backward from output layer to the input layer via hidden layer.
set of examples used only to assess the performance [generalization] of a fully specified network or to apply successfully in predicting output whose input is known.
typical Neural Network contains a large number of artificial neurons called units arranged in a series of layers.
Most Neural Networks are fully connected that means to say each hidden neuron is fully linked to every neuron in its previous layer(input) and to the next layer (output) layer.
In supervised learning, the training data is input to the network, and the desired output is known weights are adjusted until production yields desired value.
Following are the steps performed: So, these weights correspond to the learning ability of the network to classify the input patterns successfully.
The Neural network can be used to identify a unique feature of the data and classify them into different categories without any prior knowledge of the data.
Neural Network can be trained to remember the particular pattern so that when the noise pattern is presented to the network, the network associates it with the closest one in the memory or discard it.
Pattern recognition is the study of how machines can observe the environment, learn to distinguish patterns of interest from their background, and make sound and reasonable decisions about the categories of the patterns.
The design of pattern recognition systems usually involve the following three aspects- Following Neural Network architectures used for Pattern Recognition –
Fuzzy logic refers to the logic developed to express the degree of truthiness by assigning values in between 0 and 1, unlike traditional boolean logic that represents 0 and 1.
Neural networks have been successfully applied to the broad spectrum of data-intensive applications, such as: Face recognition entails comparing an image with a database of saved faces to identify the person in that input picture.
The associated assignment of face detection has direct relevance to the fact that images need to be analyzed and faces identified, earlier than they can be recognized.
President Trump’s Artificial Intelligence Executive Order: Analysis
Feb 19, 2019 Artificial intelligence (AI) recently received an endorsement from President Trump who this month signed an executive order directing federal agencies to allocate more resources for research and development, promotion and training in the emerging technology.
And, in 2017, the researcher put together a list of 80 privately-held cybersecurity companies using AI and operating in nine areas, spanning identity management to mobile, predictive, behavioral, automated, app security and more.
Of note (although it’s debatable how much), Wired noticed that in Alphabet’s (Google’s parent) latest SEC filing, it cautioned investors specifically about AI technology, warning that “new products and services, including those that incorporate or utilize artificial intelligence and machine learning, can raise new or exacerbate existing ethical, technology, legal and other challenges which may negatively affect our brands and demand for our products and services and adversely affect our revenues and operating results.” (via Wired) Six months earlier, as Wired noted, Microsoft issued a similar statement in its August SEC filing: “AI algorithms may be flawed.
These deficiencies could undermine the decisions, predictions, or analysis AI applications produce, subjecting us to competitive harm, legal liability, and brand or reputational harm.” In other words, where it concerns AI, both companies don’t yet know the full extent of what they don’t know.
Vin Narayanan Blog | News Fix: Software Startups Take Over, Florida Moves Closer To Legalizing Smokable Medical Marijuana | Talkmarkets
After more than 20 years of the internet and mobile technology serving as the driving force behind the most lucrative (and disruptive) startups, software is driving the next generation of unicorns (startups worth $1 billion), according to CB Insights –
a data and analytics firm that uses its own special sauce of machine learning and intelligence analysts to track startups and venture capital (The New York Times).
So instead the software industry is revisiting sectors that were largely ignored by the digital revolution or building software services for the Ubers and Airbnbs of the world.
CB Insights has identified startups working on farming software, scientific research collaboration and wedding planning as possibly the next generation of unicorns.
- On Saturday, October 19, 2019
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