GS-3 (Mains), Mains-2018 (English)

Deep Learning – A Subfield of Machine Learning

machine-learning-vs-deep-learning

What is Deep Learning Neural Network?

Deep Learning Neural Network is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example.

In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance.

Deep learning neural network consists:

  • Neural networks, a biologically-inspired programming paradigm which enables a computer to learn from observational data.
  • Deep learning, a powerful set of techniques for learning in neural networks.

Recently, Two new exoplanets (Kepler-90i & Kepler-80g) have been discovered using a deep learning neural networkan artificial intelligence tool that mimics the workings of a human brain.

 

Applications of deep learning neural network:

-> Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost.

-> It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers.

-> Robotics and Internet of Things (IoT) are the areas that can significantly improve our interaction with outer world.

-> Automatic Game Playing –  A recent example is AlphaGo which beat the world champion.

-> Weather forecasting where training parameters could be wind pattern, air-pressure, temperature and previous weather records of the year etc. so that it could predict weather phenomena without human intervention.

-> Automatic machine translation, deciphering complicated scripts and language modeling.

-> Examination of huge amount of space data to come out with patterns and new discoveries.

-> Restoration of old paintings, identification through low resolution images, automatic music composing etc.

 

Machine Learning vs Deep Learning:

-> Deep learning is a specialized form of machine learning. A machine learning workflow starts with relevant features being manually extracted from images. The features are then used to create a model that categorizes the objects in the image.

-> With a deep learning workflow, relevant features are automatically extracted from images. In addition, deep learning performs “end-to-end learning” – where a network is given raw data and a task to perform, such as classification, and it learns how to do this automatically.

-> Deep learning algorithms scale with data, whereas shallow learning converges. Shallow learning refers to machine learning methods that plateau at a certain level of performance when you add more examples and training data to the network.

-> A key advantage of deep learning networks is that they often continue to improve as the size of your data increases.

 

Conclusion:

Since the deep learning neural networks are essentially ‘thinking’ entities, care should be taken to employ them with caution. As human brains are capable of self-training and possibly executing nefarious activities, so is the possibility with neural networks of the future.

Also for a developing country like India, where a sizeable proportion of youth is craving for employment, excessive automation might just take away their bread. So governmental vigil is a necessity to regulate their application on a large scale.

 

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