Deep Learning

Learning Recurrent Neural Network, Applications, and its Role for Sentiment Analysis

RNNs are commonly used to investigate the sequence of data supplied in patterns, such as text recognition, various handwriting, spoken words, and genomes. Additionally, neural networks are highly implemented in numerically presented time series, which are generated by stock markets and various sensors. RNN algorithms spend time and consider data in a sequence pattern; as a result, images can be split into a collection of patches and handled as sequence data. According to extensive study, neural networks are the most powerful and versatile. Memory networks are tools, and neural networks have memory and are time demanding, therefore recurrent neural networks are employed for the same.

There are 4 types of Neural Networks:

  • One to one
  • One to many
  • Many to one
  • Many to many

Architecture and Working of Neural Network

RNN architecture is comparable to that of CNN and other artificial neural networks in that, like a normal neural network, it includes three layers: input layer, hidden layer, and output layer. Again, these layers operate sequentially. Input layers retrieve data and perform data preprocessing; after this data is filtered, it is moved to hidden layers, where several neural networks as algorithms and activation functions are performed to retrieve useful information; finally, this set of information is sent to the output layer.

The Hidden layer, which stores and remembers information about a sequence, is the most important component of RNN. Consider a network with one input layer, four hidden layers, and one output layer. Each hidden layer has its own weights and biases, which are denoted as (w1,b1), (w2,b2), (w3,b3), and (w4,b4), respectively. These weights and biases are independent of each other and thus do not retain previous information.

RNN provides the same and equal weight and bias to each layer and thus converts the independent variables to dependent variables, it reduces the parameters and memorises each prior output by supplying each output as input to succeeding hidden layer, and thus all four layers can be connected together such that weights and biases of all hidden layers are identical, into a single recurrent layer. Recurrent neural networks feature loops for storing and executing information, although other neural networks do not have such loops that can store information. One way could possibly be that it can connect earlier knowledge to the current condition.

Recurrent neural networks feature a higher level of information endurance than traditional neural networks. RNN functionality is entirely dependent on its major component, LSTM, and the other alternative for LSTM, gated recurrent unit, or GRU, has proven to be as efficient as LSTM and often more so due to its better speed and accuracy.

Applications of Neural Network

  • Picture classification entails assigning a class to a picture, such as classifying an image of a dog and a cat using attributes that the system learns automatically.
  • Image captioning is the process of automatically adding a caption to an image, similar to how Google Photos assigns correct names to places and people.
  • Language translation, as we all know, is how Google translates one language (mostly English) into so many other languages.
  • Sentiment analysis is one of the NLP methods for determining if a statement is favourably or negatively remarked. For example, if we want to assign a rating to a movie based on user reviews such as comments, we would count all of the good and negative comments and determine the movie ratings based on the average.
  • One of the uses of RNN is handwritten digit recognition and speech recognition. Google Alexa and Amazon Echo are examples of speech recognition, where the machine understands our language and conducts actions accordingly.

Long Term Short Memory Network (LSTMN)

Because these networks were designed for long-term dependencies, the idea that distinguishes it from other neural networks is that it can remember information for a long period of time without learning, again and again, making the entire process simpler and faster. This type of recurrent neural network has an internal memory for storing data.

It, too, is a recurrent neural network with a chain-like topology, but instead of a single-layer neural network, it has four. The LSTM framework also has structural gates that can add or delete information. In LSTM, there are five architectural elements:

  • Input gate
  • Forget gate
  • Cell
  • Output gate
  • Hidden state output

Final Thoughts

This article will provide you an idea of the ideas described above as well as a good grasp of how to use recurrent neural networks. RNN is an effective and unique method of performing Deep Learning models that can be well implemented in natural language processing. We have learnt about RNN in brief, however there are more models that are improved versions of RNN.

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