Deep Learning PPT: Types, Examples and Advantages

Deep Learning PPT: Types, Examples and Advantages

Deep learning is a branch of machine learning (ML) that uses data processing with a logical structure to replicate the human brain’s function in order to uncover correlations and patterns. Deep learning, also known as deep neural networks, employs several hidden layers in the neural network, as compared to typical neural networks, which have only a few hidden layers.

To get reliable results, deep learning algorithms map inputs to previously learnt data. This technique is based on an idea that is quite close to how human brains work (biological neural networks). To arrive at a conclusion, we compare fresh information with previously collected data.

Also See: 6 Reasons Why Technology cannot replace Teachers

Types of Deep Learning:

Data generated forms three major layers (input layer, hidden layer and the output layer) of neural networks are further classified into following types:

  1. Feed-forward neural networks,
  2. CNNs,
  3.  RNNs,
  4.  Modular neural networks,
  5. Radial basis function neural networks,
  6.  Multi-layer perceptron’s, and
  7. Sequence to Sequence models

Examples of Deep Learning:

Deep learning is critical for knowledge application and knowledge-based predictions in the age of Big Data. Let’s look at some examples:

  1. Computer vision: By perceiving a visual world and deciphering its context, computer vision drives self-driving automobiles, drones, and a variety of biometric activities. It uses predetermined, labeled categories to recognize and classify photos using deep learning models.
  2. NLP: Natural language processing (NLP) algorithms examine and interpret written and verbal human language inputs. Text categorization, sentiment analysis, translation, speech recognition, and other applications are among the most common. Smart virtual assistants (Siri, Alexa, Cortana), adaptive email filters, and chatbots are all instances of NLP in action.

Advantages of Deep Learning:

  1. Automated Feature Generation
  2. Does a good job with unstructured data
  3. Increased Self-Learning Capacity
  4. Allows Distributed and Parallel Algorithms
  5. Affordability
  6. Advanced Analytics 
  7. The ability to scale

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