Deep Learning – Driving the Innovation in NLP

NLP – Natural Language Processing, Can’t rule out a very common saying i.e. “Garbage in, garbage out.” Every machine learning model needs quality data, correct, suitable & powering algorithms, and good computing power, but what gets in actually into these algorithms for training sadly remains far from reality.

Every data scientist should follow their own customized & need-based essential Guide to training the data. Some challenging natural language processing (NLP) problems with singular and simpler models do require Deep learning methods to stand out from the statistical methods.

Introduction – NLP

Natural language processing is one of the most important technologies of today’s information age. It’s everywhere and used in almost every instance in daily life like emails, machine translation, google search, virtual agents, etc. In recent times deep learning has obtained too much attraction and respect from the industry which helps nlp to avoid traditional, task-specific feature engineering. The performance across many different NLP tasks, using a single end-to-end neural model has achieved significant improvement.

NLP is a kind of technique that deals with building and refining computational algorithms for automatically analyzing and representing human language in the form of text and voice. In order to enhance the natural language processing skill-set for advancing the machine learning techniques spectrum one needs to know deep learning well. By my thinking, it is intuitive that one should get a detailed understanding of the past, present, and future of deep learning in NLP which is a golden key for extracting insights from data.

Remember recurrent neural networks models comes very handy to translate language. Through interactive exercises and using scikit-learn, TensorFlow, Keras, and NLTK libraries with one’s own skill to put all of them together and apply on real-world data. NLP-powered systems & applications like Google’s powerful search engine, recently, Amazon’s voice assistant “Alexa”, and Apple’s Siri are getting smarter day by day.

If interested to dig deeper in NLP, check out the free course from Stanford’s Natural Language Processing with Deep Learning.It is a world-class course on the topic of deep learning with NLP that too at no cost.

Deep Learning and Machine Learning Role

With the rampant spread of misinformation around AI and its bundle group i.e. machine learning, neural nets, deep learning etc. it has become easier and easier to create hype and generate fake info without reality. The hype around the technology (AI bundle) is very real but that the hype is based on small real results and more on talks.

Deep learning methods are delivering on their promise thus are very popular. Deep learning (recurrent neural models) is a powerful tool to handle linguistic recursion. The human language are normally composed of phrases with certain (most of the time fixed) structures. Recurrent neural networks are super efficient to handle sequence information.

Deep learning models with enterprise architecture can be very complementary to the success of NLP programs and projects by increasing the odds of reaching targeted outcomes. As we look at AI and the future, we get impressive and firsthand information about deep learning’s role in developing NLP solutions to the next level. The key considerations involve the creation of new robust and scalable models for positioning NLP at a competitive edge.

Machine Learning provides insights, emerging techniques, a hidden treasure in data, and their inevitable impact in transforming our lives and businesses. On specific angle deep learning powers NLP to provide a platform where innovators, technology vendors, end-users, and enthusiasts showcase the latest innovations and technologies that transform businesses and the broader society

European and South African events led with a great focus on the impact of AI on business and the broader society. Visionary speakers are exciting the local and global authorities by providing new insights into key trends, opportunities, and challenges.

Deep Neural Networks in NLP

Deep neural networks can be described as a combination of an encoder that extracts features and a decoder that converts those features into the desired output. In simpler terms, this is a concise description of the structure that forms the foundation of deep neural networks. Efficient deployment of characteristics facilitates the procurement and recognition of essential qualities.

An intriguing finding is that the mentioned idea can also be applied to the realm of processing natural language. A profound understanding of modern neural network algorithms is necessary to efficiently handle language data, which explains the reason behind it. New techniques and technologies have brought about a rapid makeover in the field of Natural Language Processing (NLP). The major driver of this advancement is largely caused by the rapid increase in the amount of accessible data and demand for such tools.

Points to Note:

All credits if any remain on the original contributor only. We have covered all basics around NLP. RNNs are all about modeling units in sequence. The perfect support for Natural Language Processing – NLP tasks. Though often such tasks struggle to find the best companion between CNN’s and RNNs’ algorithms to look for information.

Books + Other readings Referred

• Research through open internet, news portals, white papers and imparted knowledge via live conferences & lectures.
• Lab and hands-on experience of  @AILabPage (Self-taught learners group) members.
• This useful pdf on NLP parsing with Recursive NN.
• Amazing information in this pdf as well.

Feedback & Further Question

Do you have any questions about Deep Learning or Machine Learning? Leave a comment or ask your question via email. Will try my best to answer it.

Conclusion –  Deep learning under the unsupervised learning domain works much better and given the current scale of data, this makes even more sense. Deep Learning, in short, is going much beyond machine learning and its algorithms that are either supervised or unsupervised. In DL it uses many layers of nonlinear processing units for feature extraction and transformation.

In DL learning is based on multiple levels of features or representation in each layer with the layers forming a hierarchy of low-level to high-level features Where traditional machine learning focuses on feature engineering, deep learning focuses on end-to-end learning based on raw features. Traditional deep learning creates/ train-test splits of the data where ever possible via cross-validation. Load ALL the training data into the main memory and compute a model from the training data.

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