Natural Language Processing (NLP) is a subfield of Artificial Intelligence that focuses on the interaction between computers and humans through natural language. It enables machines to understand and interpret the human language, enabling various applications such as speech recognition, sentiment analysis, language translation, and more. In recent years, NLP has gained significant attention due to its vast potential and impact in various industries.

TensorFlow and Keras are popular libraries in the field of machine learning and deep learning, offering powerful tools and APIs for developing NLP models efficiently. This article aims to explore the theory and applications of NLP using TensorFlow and Keras.

to Natural Language Processing

Natural Language Processing involves the ability of computers to understand and interpret human language, both written and spoken, and respond accordingly. It goes beyond simple keyword matching and focuses on understanding the context, sentiment, and meaning of the language used.

Natural Language Processing Crash Course for Beginners: Theory and Applications of NLP using TensorFlow 2.0 and Keras

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With the exponential growth of data and the increasing need to analyze and extract insights from unstructured text, NLP has become essential in various industries. It unlocks opportunities to automate tasks, improve customer interaction, enhance search engines, and even aid in healthcare and finance sectors.

TensorFlow and Keras for NLP

TensorFlow, developed by Google, is an open-source library primarily used for numerical computation and building machine learning models. It provides high-level APIs for creating and training deep neural networks, making it a go-to choice for NLP tasks.

Keras, on the other hand, is a user-friendly neural network library written in Python. It simplifies the process of building and training deep learning models by offering a clear and intuitive interface. Keras can be used as a frontend to TensorFlow, allowing developers to leverage the power of TensorFlow with ease.

Together, TensorFlow and Keras provide a comprehensive environment for developing NLP models with ease and efficiency.

Theoretical Foundations of NLP

In order to dive deeper into NLP using TensorFlow and Keras, it's important to understand some of the foundational concepts.

1. Tokenization:

Tokenization is the process of breaking down textual data into smaller units called tokens. It helps in transforming unstructured text into a structured format that can be easily understood by machines. The tokens can be words, sentences, or even characters.

2. Word Embeddings:

Word embeddings represent words in a continuous vector space, allowing machines to understand the semantic meaning behind them. It captures relationships and similarities between words, enabling more effective NLP models.

3. Recurrent Neural Networks (RNN):

RNNs are a type of neural network well-suited for sequential data, such as natural language. They have recurrent connections that allow information to persist, making them capable of understanding context and dependencies between words in a sentence.

4. Long Short-Term Memory (LSTM):

LSTMs are a specialized type of RNN that addresses the vanishing gradient problem. They have gated cells that can selectively retain or forget information, enabling better long-term memory and handling of sequences with long dependencies.

Applications of NLP using TensorFlow and Keras

NLP using TensorFlow and Keras finds application in a wide range of domains. Here are a few notable applications:

1. Sentiment Analysis:

Sentiment analysis aims to determine the sentiment or emotional tone behind a piece of text. It is widely used in social media monitoring, customer feedback analysis, and brand reputation management.

2. Language Translation:

NLP models can be trained to perform language translation tasks. This is especially useful in scenarios where real-time translation is required, such as communication between individuals speaking different languages or translation of foreign text.

3. Named Entity Recognition (NER):

NER involves identifying and classifying named entities within text, such as names of people, organizations, locations, etc. It is useful in various applications, including information extraction, question-answering systems, and document classification.

4. Text Generation:

NLP models can be used to generate text based on given input or patterns. This finds application in chatbots, virtual assistants, and even creative writing.

5. Speech Recognition:

NLP techniques combined with deep learning can be used to build accurate and efficient speech recognition systems. These systems are used in voice assistants, voice-activated devices, and transcription services.

Natural Language Processing, backed by powerful libraries like TensorFlow and Keras, opens up numerous possibilities in understanding and interpreting human language. From sentiment analysis to language translation, the applications of NLP are vast and diverse. By harnessing the theoretical foundations and leveraging the capabilities of TensorFlow and Keras, developers can build advanced NLP models and unlock the power of natural language understanding.

Natural Language Processing Crash Course for Beginners: Theory and Applications of NLP using TensorFlow 2.0 and Keras

by AI Publishing(Kindle Edition)

4.3 out of 5

Language	:	English
File size	:	11506 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	396 pages
Lending	:	Enabled

FREE

Natural Language Processing Crash Course for Beginners

Artificial Intelligence (AI) isn’t the latest fad!  The reason is AI has been around since 1956, and its relevance is evident in every field today.
Artificial Intelligence incorporates human intelligence into machines. Machine Learning (ML),a branch of AI, enables machines to learn by themselves. Deep Learning (DL),a subfield of Machine Learning, uses algorithms that are inspired by the functioning of the human brain.

Natural Language Processing (NLP) combines computational linguistics and Artificial Intelligence, enabling computers and humans to communicate seamlessly. And NLP is immensely powerful and impactful as every business is looking to integrate it into their day to day dealings.

How Is This Book Different?

This book by AI Publishing is carefully crafted, giving equal importance to the theoretical concepts as well as the practical aspects of natural language processing. In each chapter of the second half of the book, the theoretical concepts of different types of deep learning and NLP techniques have been covered in-depth, followed by practical examples.  

You will learn how to apply different NLP techniques using the TensorFlow and Keras libraries for Python. Each chapter contains exercises that are designed to evaluate your understanding of the concepts covered in that chapter. Also, in the Resources section of each chapter, you can access the Python notebook. The author has also compiled a list of hands-on NLP projects and competitions that you can try on your own.

The main benefit of purchasing this book is you get immediate access to all the extra learning material presented with this book—Python codes, exercises, PDFs, and references—on the publisher’s website without having to spend an extra cent. You can download the datasets used in this book at runtime, or you can access them in the Resources/Datasets folder.

The author holds your hand through everything. He provides you a step by step explanation of the installation of the software needed to implement the various NLP techniques in this book. You can start experimenting with the practical aspects of NLP right from the beginning.

Even if you are new to Python, you’ll find the ultra-short course on Python programming language in the second chapter immensely helpful. You get all the codes and datasets with this book. So, if you have access to a computer with the internet, you can get started. 

The topics covered include:

• What is Natural Language Processing?
• Environment Setup and Python Crash Course
• to Deep Learning
• Text Cleaning and Manipulation
• Common NLP Tasks
• Importing Text Data from Various Sources

• Word Embeddings: Converting Words to Numbers
• IMDB Movies Sentimental Analysis
• Ham and Spam Message Classification
• Text Summarization and Topic Modeling
• Text Classification with Deep Learning
• Text Translation Using Seq2Seq Model
• State of the Art NLP with BERT Transformers
• Hands-on NLP Projects/Articles for Practice
• Exercise Solutions

Click the BUY button and download the book now to start your Natural Language Processing journey.