Neural Network and its Use Cases
Hello Guys !!!
Today, neural networks are used for solving many business problems such as sales forecasting, customer research, data validation, and risk management. For example, at Statsbot we apply neural networks for time-series predictions, anomaly detection in data, and natural language understanding.
So , Lets Learn about Neural Network and Its usecases…..
What is Neural Network ??
Neural networks are computing systems with interconnected nodes that work much like neurons in the human brain. Using algorithms, they can recognize hidden patterns and correlations in raw data, cluster and classify it, and over time continuously learn and improve.
Why Neural Network??
Most of major IT companies use them to make their services more useful and to create natural reaction to users behavior. Neural networks are the basis of many image recognition and speech synthesis systems. They are used in some navigation systems, algorithms of industrial robots or unmanned vehicles.
Types of Neural Networks in Deep Learning
Three important types of neural networks that form the basis for most pre-trained models in deep learning:
- Artificial Neural Networks (ANN)
- Convolution Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
👉Artificial Neural Networks (ANN)
A single perceptron (or neuron) can be imagined as a Logistic Regression. Artificial Neural Network, or ANN, is a group of multiple perceptrons/ neurons at each layer. ANN is also known as a Feed-Forward Neural network because inputs are processed only in the forward direction.
ANN consists of 3 layers — Input, Hidden and Output.
The input layer accepts the inputs, the hidden layer processes the inputs, and the output layer produces the result. Essentially, each layer tries to learn certain weights.
ANN can be used to solve problems related to:
- Tabular data
- Image data
- Text data
👉Convolution Neural Networks (CNN)
Convolutional neural networks (CNN) are all the rage in the deep learning community right now. These CNN models are being used across different applications and domains, and they’re especially prevalent in image and video processing projects.
- CNN learns the filters automatically without mentioning it explicitly. These filters help in extracting the right and relevant features from the input data
👉Recurrent Neural Networks (RNN)
A recurrent neural network (RNN) commonly used in speech recognition and natural language processing (NLP). RNNs are designed to recognize a data’s sequential characteristics and use patterns to predict the next likely scenario.
We can use recurrent neural networks to solve the problems related to:
- Time Series data
- Text data
- Audio data
Now Lets Study a Use Case of Neural Network….
GOOGLE NEURAL MACHINE TRANSLATION-(GNMT)
Google Neural Machine Translation (GNMT) is a neural machine translation (NMT) system developed by Google and introduced in November 2016, that uses an artificial neural network to increase fluency and accuracy in Google Translate.
How GNMT works ???
Many of you may already rely on Google Translate to get quick, relatively accurate translations, perhaps for Spanish or Chinese documents into English — now offering broad support for 130 languages.
The accuracy of Google’s translation software was given a giant boost in September of 2016 with the introduction of Google Neural Machine Translation (GNMT) technology. Based on the latest advances in artificial intelligence machine learning, it enables ‘zero-shot translation,’ i.e., translation of languages never before seen by the Google Translate —
For example, Japanese to Korean when the system only has information about English to Japanese and Korean. –This helped reduce translation errors by up to 90%. GNMT is moving at a steady pace in 2017 to upgrade all Google Translate systems for different language pairs.
Such a great innovation is modified and updated for giving better version of Google Translate to users by Using Neural Network concepts.
Thank You…!!!!
