Neural networks use cases in industries.
Hello guys,
I have come up with a new blog on what neural network is and how they are being used in the industries.
What is Neural Networks?
Neural networks are a means of doing machine learning, in which a computer learns to perform some tasks by analysing training examples. Usually, the examples have been hand-labelled in advance. An object recognition system, for instance, might be fed thousands of labelled images of cars, houses, coffee cups, and so on, and it would find visual patterns in the images that consistently correlate with labels.
Modelled loosely on the human brain, a neural net consists of thousands or even millions of simple processing nodes that are densely interconnected. Most of today’s neural nets are organized into layers of nodes, and they’re “feed-forward,” meaning that data moves through them in only one direction. An individual node might be connected to several nodes in the layer beneath it, from which it receives data, and several nodes in the layer above it, to which it sends data.
To each of its incoming connections, a node will assign a number known as a “weight.” When the network is active, the node receives a different data item — a different number — over each of its connections and multiplies it by the associated weight. It then adds the resulting products together, yielding a single number. If that number is below a threshold value, the node passes no data to the next layer. If the number exceeds the threshold value, the node “fires,” which in today’s neural nets generally means sending the number — the sum of the weighted inputs — along all its outgoing connections.
When a neural net is being trained, all of its weights and thresholds are initially set to random values. Training data is fed to the bottom layer — the input layer — and it passes through the succeeding layers, getting multiplied and added together in complex ways, until it finally arrives, radically transformed, at the output layer. During training, the weights and thresholds are continually adjusted until training data with the same labels consistently yield similar outputs.
Feed propagation and Back Propagation remember that Forward Propagation is the process of moving forward through the neural networks. After the weight is updated and we give it to the neural. Our goal in neural nets is to arrive the point of least error as fast as possible.
Back Propagation is the reverse. Except instead of signal, we are moving error backwards through our model i.e. input layer.
Tasks Neural Networks Perform
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.
Neural networks are fundamental to deep learning, a robust set of NN techniques that lends itself to solving abstract problems, such as bioinformatics, drug design, social network filtering, and natural language translation. Deep learning is where we will solve the most complicated issues in science and engineering, including advanced robotics.
Use Cases of Neural networks in:
Paige.AI
Paige was launched in early 2018 in USA to bring technology created at Memorial Sloan Kettering to the world. We are developing novel deep learning algorithms based on convolutional and recurrent neural networks as well as generative models that are able to learn efficiently from an unprecedented wealth of visual and clinical data. The company builds powerful; clinical-grade computational technologies to transform the diagnosis; treatment and biomarker discovery for cancer. With AI positioned to open a new future of pathology; Paige has created an AI-native digital pathology ecosystem that enables the Pathologist to achieve higher quality; faster throughput and lower cost diagnoses and treatment recommendations. Additionally; Paige accelerates new biomarker discovery; and is built to generate new insights into pathways and drug efficacy. Paige is committed to delivering the technology in a way that integrates seamlessly with hospital workflow systems and ensures safety; accuracy; and data privacy.
Peltarion
Peltarion is a software engineering company in Sweden established in 2005, is making Artificial Intelligence accessible and affordable for every company in the world. Peltarion aims to apply neural networks to real-world problems in a wide variety of fields, ranging from building control systems for heavy industry to helping to analyze the migration of dolphins in the Pacific Ocean.
LeapMind
LeapMind is Japanese company making deep learning “small and compact” and accessible across a broad spectrum of applications, evolving the Internet of Things into the “Deep Learning of Things (DoT).” While improving the accuracy of neural network models, they are researching and developing their own innovative algorithms that can reduce the computational complexity of deep learning to use within a small computing environment. Leap Mind also conducts research on original chip architectures that can efficiently implement deep neural networks on a circuit with low power and limited memory.
TwentyBN
Founded in 2015 in Germany, TwentyBN is committed to bringing the progress of AI machines with a human-like “awareness” into every corner of society. We grow the world’s largest commercial video data studio with millions of high-quality video data and train neural networks to internalize visual common sense and grasp complex, dynamic scenes. TwentyBN’s deep learning software works in real time at the edge to enable truly touchless human-machine interaction and empower the next-generation intelligent systems to decipher human actions.
Thankyou!!
Sources:
https://www.investopedia.com/terms/n/neuralnetwork.asp#:~:text=Neural%20networks%20are%20a%20series,fraud%20detection%20and%20risk%20assessment.Top neural networks companies | VentureRadarTop companies for neural networks at VentureRadar with Innovation Scores, Core Health Signals and more. Including…
