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Introduction to Recursive Neural Network: Concept, Principle & Implementation

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11th Sep, 2020
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Introduction to Recursive Neural Network: Concept, Principle & Implementation

A Recursive Neural Network is a type of deep neural network. So, with this, you can expect & get a structured prediction by applying the same number of sets of weights on structured inputs. With this type of processing, you get a typical deep neural network known as a recursive neural network. These networks are non-linear in nature.

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The recursive networks are adaptive models that are capable of learning deep structured erudition. Therefore, you may say that the Recursive Neural Networks are among complex inherent chains. Let’s discuss its connection with deep learning concepts. 

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Concept of Deep Learning

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One cannot deny the factor that Deep Learning is an amazing technique of machine learning.

It has taught even computers how to behave & respond naturally, similar to what a human being is supposed to do; the same teachings are hypothetically programmed into computers these days. Hence, they always have to follow an example & learn through it. So, if anyone wants to predict any unpredictable thing, it is now possible through Deep learning.

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Many researchers & even engineers are quite busy with the development of artificial intelligence. They are using a combination of non-bio-neural networks along with natural intelligence to sort all the workarounds. We can, therefore, say that now Deep Learning is going almost beyond machine learning. In fact, also along with its algorithms techniques, which are both supervised or even unsupervised.

Many layers of non-linear processing units are utilized for these tasks, such as extraction of features & certain transformations in Deep Learning. This has become a revolution in current industries because its demonstration capabilities are very near to that of human-level capabilities & accuracies in most of the tasks it performs. So, if we talk about the task such as pattern recognition, or if we say an image classification, not only this but including voice or text decoding are also possible with so many more such options with deep learning algorithms.

Deep Learning is among certain key technologies nowadays that are highly used to control voice commands in mobile devices such as smartphones, android TVs, Alexa voice command enabled speakers & a lot more similar devices. We introduced even driverless cars through deep learning technology. This has enabled them in recognition of various image processing, such as stop signs. It has also made them learn to distinguish even images such as a pedestrian coming from a far lamppost.

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Therefore, DL has made its name already in fields like image processing & image classification. It has also effectively recognized speech with high accuracy. Hence, if we say that Deep learning technology is paving its path to a crucial success rate, we won’t be wrong. It has grabbed the attention of all living beings with a good notation.

It has countlessly proved its potential by achieving a lot of results, which seems to be impossible earlier. Here, the business & known developer communities’ leaders must come forward and get a thorough analysis in its further potential to bring out the potency on what it can do and how NLP & deep learning may benefit humans in all areas.

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Recursive Neural Network 

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In simple words, if we say that a Recursive neural network is a family person of a deep neural network, we can validate it. So, if the same set of weights are recursively applied on a structured input, then the Recursive neural network will take birth. So, it will keep happening for all the nodes, as explained above. Recursive neural networks are made of architectural class, which is majorly operational on structured inputs. The RNN’s are particularly directed on acyclic graphs.

It’s a deep tree structure. For conditions like there are needs to parse the complete sentence, there recursive neural networks are used. It has a topology similar to tree-like. The RNN’s allow the branching of connections & structures with hierarchies.

They mainly use recursive neural networks for the prediction of structured outputs. It is done over variable-sized input structures. Also, it traverses a given structure that too in topological order. They also do it for scalar predictions. But here point to note is that the Recursive neural network just does not respond to structured inputs, but it also works in contexts.

Each time series is processed separately. A very interesting point to ponder is that the first introduction of RNN happened when a need arose to learn distributed data representations of various structural networks. For instance, logical terms.

Recurrent Neural Network vs. Recursive Neural Networks

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As per the sources mentioned in Wikipedia, the recurrent neural network is a recursive neural network. Both the neural networks are denoted by the same acronym – RNN. If neural networks are recurring over a period of time or say it is a recursive networking chain type, it is a recurrent neural network. To generalize, it belongs to the recursive network.

The above image depicts the recursive neural network. Here, if you see, you will find that each of the parent nodes, its children are a node quite similar to the parent node. Therefore, it’s evident that the recurrent neural network is more similar to a hierarchical network type. You can see clearly that there is no concept of structured input & output processing here. It is just performed in a tree-like hierarchical manner where there are no time specifications & dependencies associated.

Hence, the major difference between the recursive neural network and recurrent neural networks is clearly not very well defined. It is seen that the efficiency of any recursive neural network is far better compared to a feed-forward network. Recurrent neural networks are created in a chain-like structure. There are no branching methods, but the recurrent neural networks are created in the form of a deep tree structure.

Recursive Neural Networks | Principle defined

So, to generalize here, the Recurrent networks do not differ from Recursive neural networks. But in fact, it is a Recursive neural network. There is a fact related to that recursive networks are inherently complex and, therefore, not accepted on a broader platform. These RNN’s are even more expensive at all computational learning stages & phases.

You can learn these computational models that are suited very well for classification as well as regression problems. Get enrolled in the upGrad online course today to know how it can solve any supervised or unsupervised learning tasks. Get enrolled today! Learn from NLP & machine learning experts.

Recursive Neural Network Implementation

We use a Recursive Neural Network for sentiment analysis in sentences. Sentiment analysis of sentences is among the major tasks of NLP (Natural Language Processing), that can identify writers writing tone & sentiments in any specific sentences. When a writer expresses any sentiments, basic labels around the tone of writing are identified. For instance, whether the meaning is a constructive form of writing or negative word choices.

For instance, in the undermentioned case of the variable dataset, it expresses every emotion in distinctive classes.

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So, if you see the above image for the Sentiment analysis, it is completely implemented with the help of Recursive Neural Networks algorithms. The RNN is a form of a recursive neural net that has a tree structure. 

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Conclusion

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We hope this article has cleared some basics of Deep learning & recursive neural network algorithms. The knowledge of machine learning algorithms & its type can help anyone understand how much potential it holds for future revolution.

By learning machine language algorithms, you may get an idea of computational processing on datasets, their quality despite their nature & sizes. By acquiring these learnings, one can extract more relevant & useful information from a dataset used as a useful resource. So, go ahead. Learn a machine language algorithm this season. Don’t worry; we assure you it will not complicate your basic knowledge on coding or logical terms but will help you enhance all NPL specifications.

If you’re interested to learn more about machine learning, check out IIIT-B & upGrad’s PG Diploma in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.

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Pavan Vadapalli

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Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.
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Frequently Asked Questions (FAQs)

1What are recursive neural networks used for?

Recursive neural networks are a kind of deep learning network. They are more general, and more powerful than feedforward neural networks. The word recursive means that the neural network is applied to its own output. Recurrent neural networks are used for sequence labeling problems. They are designed to recognize patterns within the data that carry information from the past. In other words, the recursive neural network learns from the past and processes new data based on the experience. The recursive neural network uses learning algorithms to determine how to make the appropriate changes in the future.

2What is the difference between CNN and RNN?

CNN stands for Convolutional Neural Network. CNN is a special kind of neural network that are capable of taking in sequential data and understanding patterns. CNNs are usually used for image recognition but have been used in problems as complex as generating language from unlabeled data. You can read more about CNN's here. RNN stands for Recurrent Neural Network. RNN's are very similar to regular neural networks except they have a built-in memory, kind of like a loop. They can be used to model sequences like language or text data. Like CNN's, there are many different kinds of RNN's, but LSTMs are one of the most popular.

3Why is RNN used for machine translation?

Recurrent Neural Networks (RNNs) are used in machine translation because they capture the future output given a sequence of inputs. For instance, the word “rundog” without the past context, has no meaning. RNNs capture this context and translate dog to canine. Without RNNs machine translators cannot make inferences about the input. This is why RNNs are used.

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