Delving more into Deep Learning!
NLP and Deep Learning in a Nutshell
Natural Language Processing is simply defined as the automation developed with the algorithms that make the natural language understandable by the machine.
Deep learning is a subset of machine learning that imitates the working of the human brain in processing data through artificial neural networks.
Refer to the following article to read more about NLP:
8 Most popular Deep Learning Frameworks for NLP
Today, we have state of the art NLP models with an accuracy close to 98%. NLP has helped to bridge the gap between humans and machines and it will continue to do so. Have a look over top 8 NLP frameworks:
TensorFlow remains one of the most popular NLP frameworks. It was developed by the Google Brain team in 2015. Companies using TensorFlow at production scale include the likes of Airbus, Intel, IBM, Twitter, etc. TensorFlow is known for its flexible, production-scale architecture capable of running on both CPU and GPU. Google Translate is its most famous application. Other applications are text classification and speech recognition.
PyTorch is an open-source deep-learning library based on Torch and it is famous for its speed and flexibility. PyTorch generally uses Recurrent Neural Networks (RNN) for classification, machine translation, text generation, tagging, and other NLP tasks. It provides tensor computation with strong GPU acceleration.
Keras is a high-level neural network-based Python API and therefore, it is accessible for a variety of data scientists working in the field of deep learning and NLP. It supports all the common NLP functions like parsing, machine translation, and classification. Both Convolutional Neural Networks and Recurrent Neural Networks (CNN & RNN) are compatible with Keras.
Several developers believe that SpaCy is the best way to prepare the text for deep learning. Most of the python libraries are supported by SpaCy including Scikit-learn. SpaCy framework is employed by diverse companies to render cutting-edge NLP software. It is fast and agile and it allows developers a more natural path to NLP tasks such as entity recognition.
Stanford’s CoreNLP can run as a simple web service and it provides APIs for some of the most common programming languages including Python. It is acclaimed for its intuitive syntax and is the most sought after modern grammatical analysis tool. It can perform multiple NLP tasks like sentiment analysis, bootstrapped pattern learning, named entity recognition across 53 languages, and part-of-speech (POS) tagging.
Chainer is a powerful Python-based framework for neural networks. It was developed by Preferred Networks, a Japanese company in 2014. It was released under the MIT silence and it supports the history of computation. Different types of neural networks are used in Chainer like CNN, RNN, and sequence to sequence models. It is a standalone framework for deep learning and comes in handy with various NLP models.
Theano enables users to create their machine learning models and it offers numerous tools to perform NLP tasks. It is a numerical computation Python library which lets you perform machine translation, speech recognition, word embedding, and text classification. You can define, optimize, and evaluate mathematical expressions in Theano, especially ones with multi-dimensional arrays.
Familiar with regression and classification algorithms in machine learning? Scikit-learn is an excellent framework for implementing these algorithms while working with big data. Honestly, Scikit-learn is for beginners and it provides a baseline of ML algorithms to get started with some small-scale NLP projects.
So far we have seen all the popular deep learning frameworks for NLP. While TensorFlow, PyTorch, and Keras have been making most of the noises in the Deep Learning field, we shouldn’t forget that it takes less than 24 hours for a technology to get replaced with another. Hence, we suggest you keep an eye on the emerging trends and Create Bytes is always there to deliver regular tech updates at your doorstep. See you again!
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