Natural Language Processing in TensorFlow from Coursera

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.

In Course 3 of the deeplearning.ai TensorFlow Specialization, you will build natural language processing systems using TensorFlow. You will learn to process text, including tokenizing and representing sentences as vectors, so that they can be input to a neural network. You’ll also learn to apply RNNs, GRUs, and LSTMs in TensorFlow. Finally, you’ll get to train an LSTM on existing text to create original poetry!

The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

What's inside

Syllabus

Sentiment in text

The first step in understanding sentiment in text, and in particular when training a neural network to do so is the tokenization of that text. This is the process of converting the text into numeric values, with a number representing a word or a character. This week you'll learn about the Tokenizer and pad_sequences APIs in TensorFlow and how they can be used to prepare and encode text and sentences to get them ready for training neural networks!

Word Embeddings

Last week you saw how to use the Tokenizer to prepare your text to be used by a neural network by converting words into numeric tokens, and sequencing sentences from these tokens. This week you'll learn about Embeddings, where these tokens are mapped as vectors in a high dimension space. With Embeddings and labelled examples, these vectors can then be tuned so that words with similar meaning will have a similar direction in the vector space. This will begin the process of training a neural network to understand sentiment in text -- and you'll begin by looking at movie reviews, training a neural network on texts that are labelled 'positive' or 'negative' and determining which words in a sentence drive those meanings.

Sequence models

In the last couple of weeks you looked first at Tokenizing words to get numeric values from them, and then using Embeddings to group words of similar meaning depending on how they were labelled. This gave you a good, but rough, sentiment analysis -- words such as 'fun' and 'entertaining' might show up in a positive movie review, and 'boring' and 'dull' might show up in a negative one. But sentiment can also be determined by the sequence in which words appear. For example, you could have 'not fun', which of course is the opposite of 'fun'. This week you'll start digging into a variety of model formats that are used in training models to understand context in sequence!

Sequence models and literature

Taking everything that you've learned in training a neural network based on NLP, we thought it might be a bit of fun to turn the tables away from classification and use your knowledge for prediction. Given a body of words, you could conceivably predict the word most likely to follow a given word or phrase, and once you've done that, to do it again, and again. With that in mind, this week you'll build a poetry generator. It's trained with the lyrics from traditional Irish songs, and can be used to produce beautiful-sounding verse of it's own!

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches practices for using TensorFlow, a popular framework for machine learning

Specifically designed for software developers

Taught by industry recognized experts

Covers text processing and representation

Includes hands-on labs in TensorFlow

Reviews summary

Nlp with tensorflow

learners say NLP with TensorFlow is a great course for hands-on experience and for beginners. It is engaging and the concepts are broken down into smaller sections, making it easier to understand. The course is a practical implementation of concepts learned in Deep Learning and it has helped them to understand how the basics of NLP work. They also liked that an autograder was implemented on the exercise problems. Some found the course to be a bit too basic and lacking in theory, while others felt like they had to jump to many concepts into another course. Overall, learners found this course to be a great introduction to NLP in TensorFlow.

learners say the autograder helped with understanding the material.

"I really enjoyed this course."

"For me it was a great introduction to the topic."

"Well explained and it was also fun to try the examples."

learners say this course is a good in depth analysis of embedding and text preprocessing for text generation and classification.

"good in depth analysis of the tensorflow layers(embedding) and text preprocessing(tokenizing) for text generation and classification"

The ungraded exercises are considered an important note by learners, as they allow them to put their learning into practice.

"Nice lecture, Thanks to Laurence i can understand about total concept of NLP."

"I want to study about Deep learning on Coursera."

This course is largely positive for beginners, as it provides easy-to-understand introductions to topics and a step-by-step head start into NLP's practical aspects using the amazing TensorFlow framework.

"The course is very well set up and maintained."

"I had a blast completing it..."

"Everything was explained perfectly."

"Thank you, Laurence and Andrew!"

"It's an amazing experience!"

learners say that TensorFlow in this specialization was more into practical examples and they hope to see more theory behind the course and more challenging graded assessment.

"This is a very good course for beginners, but this course only focused on practical examples."

"I hope there are more theory behind the course and also the more challenging grade assessment."

learners say there are some parts that are less detailed in defining a concept. Other than that everything is pretty good and easy to understand.

"I feel that there are some parts that are less detailed in defining a concept."

"other than that everything is pretty good and easy to understand"

learners say this course is a bit too basic and there could be more focus on choosing parameters. The course feels too shallow.

"I would like to implement programming exersice with rate because you can discuss with all the community and you enrich your knowledge."

"It's shallow, does not explain some important concepts."

"Seems like it was explained on a rush, or didn't bother going more into detaisl."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Natural Language Processing in TensorFlow with these activities:

Review preprocessing fundamentals

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Refreshing knowledge of data preprocessing basics will help you quickly get up to speed with tokenizing and embedding text.

Browse courses on Data Preprocessing

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Review the basics of text tokenization
Practice tokenizing text using common techniques
Learn about word embeddings and their role in NLP

Compile a study guide on TensorFlow and NLP

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Creating a study guide will help you synthesize and retain the key concepts covered in this course.

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Review your notes and assignments from the course
Identify the most important concepts and techniques
Organize the information into a logical and cohesive guide

Explore TensorFlow tutorials on tokenization and embedding

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TensorFlow provides excellent tutorials to help you understand how to prepare text data for NLP models.

Browse courses on Tokenization

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Complete TensorFlow's tutorial on tokenizing text
Explore TensorFlow's guide to word embeddings
Apply what you've learned to prepare text data for your own NLP project

Five other activities

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Practice using Tokenizer and pad_sequences APIs in TensorFlow

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Practice using the Tokenizer and pad_sequences APIs will help you develop proficiency in preparing text data for neural networks.

Browse courses on TensorFlow

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Use the Tokenizer API to tokenize a sample text dataset
Apply the pad_sequences API to prepare the tokenized data for training
Experiment with different tokenization and padding techniques to optimize your models

Read 'Natural Language Processing with TensorFlow' by Thushan Ganegedara

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This book provides a comprehensive overview of NLP techniques implemented using TensorFlow.

View TensorFlow in Action on Amazon

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Read the book and take notes on key concepts
Complete the exercises and projects in the book
Discuss the book's content with other students or professionals

Build a simple text classification model using TensorFlow

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Building a text classification model will reinforce your understanding of the concepts covered in this course.

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Define your dataset and preprocessing pipeline
Create a neural network model for text classification
Train and evaluate your model
Deploy your model and analyze its performance

Mentor other students in NLP or TensorFlow

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Mentoring others will deepen your understanding of the concepts and improve your communication skills.

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Identify opportunities to mentor other students in NLP or TensorFlow
Prepare materials and resources to support your mentees
Provide guidance and feedback to your mentees on a regular basis

Participate in an NLP or TensorFlow competition

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Participating in NLP or TensorFlow competitions will push you to apply your skills and improve your problem-solving abilities.

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Identify an appropriate NLP or TensorFlow competition to participate in
Develop a strategy for solving the competition problem
Implement your solution and submit it for evaluation
Analyze your results and identify areas for improvement

Career center

Learners who complete Natural Language Processing in TensorFlow will develop knowledge and skills that may be useful to these careers:

Natural Language Processing Engineer

Working as a Natural Language Processing Engineer, you would be responsible for developing AI-powered algorithms for analyzing and processing text and natural language data. The 'Natural Language Processing in TensorFlow' course can be crucial in your journey to become a Natural Language Processing Engineer. This course provides practical, hands-on guidance on utilizing TensorFlow, an industry-standard framework, to build and deploy NLP systems, including sentiment analysis, text tokenization, and language generation.

See salaries and explore the career path for Natural Language Processing Engineer

NLP Researcher

NLP Researchers focus on advancing the field of Natural Language Processing through theoretical and practical research. Taking the 'Natural Language Processing in TensorFlow' course will expose you to state-of-the-art NLP techniques and methodologies. By completing this course, you'll gain a deeper understanding of NLP concepts and contribute to the ongoing research in this field.

See salaries and explore the career path for NLP Researcher

Natural Language Generation Engineer

Natural Language Generation Engineers develop systems that generate human-like text. This course, 'Natural Language Processing in TensorFlow,' provides a solid foundation in NLP and machine learning techniques for generating natural language. By completing this course, you'll gain the skills to build NLG systems for various applications, such as chatbots, text summarization, and language translation.

See salaries and explore the career path for Natural Language Generation Engineer

Computational Linguist

Computational Linguists combine linguistic knowledge with computer science to analyze and process natural language. The 'Natural Language Processing in TensorFlow' course will help you develop the technical skills necessary to build NLP systems, enabling you to analyze, interpret, and generate human language. This course can provide a valuable foundation for a career in Computational Linguistics.

See salaries and explore the career path for Computational Linguist

Data Scientist

Data Scientists are responsible for developing and utilizing data-driven solutions to solve business problems. As a career path, the 'Natural Language Processing in TensorFlow' course can help you build a solid foundation in applying NLP techniques to real-world datasets. This course will teach you how to process, analyze, and extract meaningful insights from text data, a critical skill for Data Scientists.

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Speech Recognition Engineer

Speech Recognition Engineers develop and improve speech recognition systems. The 'Natural Language Processing in TensorFlow' course can help you build a foundation in NLP and machine learning, essential for developing speech recognition systems. You'll learn techniques for processing and analyzing speech data, enabling you to contribute to the advancement of speech recognition technology.

See salaries and explore the career path for Speech Recognition Engineer

Information Retrieval Specialist

Information Retrieval Specialists design and implement systems for searching and retrieving information from large datasets. By taking the 'Natural Language Processing in TensorFlow' course, you'll gain expertise in applying NLP techniques to improve the accuracy and efficiency of information retrieval systems. This course can enhance your ability to develop innovative search engines and other information retrieval applications.

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Machine Learning Engineer

Machine Learning Engineers apply machine learning techniques to solve real-world problems. This course will help you develop the skills necessary to build and deploy ML models that can process and analyze text data. By completing this course, you'll gain a competitive edge in applying NLP to enhance the accuracy and effectiveness of your ML solutions.

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Software Engineer

Software Engineers design, develop, and maintain software applications. The 'Natural Language Processing in TensorFlow' course can provide you with specialized knowledge and skills in NLP, enabling you to develop software solutions that can analyze, process, and generate natural language. This course will help you stay competitive in the industry and create innovative NLP-based applications.

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Business Intelligence Analyst

Business Intelligence Analysts help organizations make data-driven decisions. This course will provide you with the skills to analyze text data, extract insights, and communicate them effectively to stakeholders. The knowledge gained from this course can enhance your ability to identify trends, patterns, and opportunities within text data, driving better decision-making within organizations.

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Product Manager

Product Managers oversee the development and launch of new products. The 'Natural Language Processing in TensorFlow' course can provide you with a valuable understanding of NLP techniques and their applications in product development. By incorporating NLP into your product strategy, you can improve user experience, streamline product development, and gain a competitive advantage.

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UX Designer

UX Designers focus on creating user-friendly and intuitive digital experiences. By taking the 'Natural Language Processing in TensorFlow' course, you'll gain knowledge in NLP techniques that can enhance the user experience of digital products. You'll learn how to analyze user feedback, conduct user research, and apply NLP to improve the overall user experience.

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Content Strategist

Content Strategists plan and execute content strategies to achieve specific business goals. The 'Natural Language Processing in TensorFlow' course can provide you with the skills to analyze text data, understand user preferences, and create effective content strategies. By leveraging NLP techniques, you'll be able to optimize content for search engines, social media, and other digital channels, maximizing its reach and impact.

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Technical Writer

Technical Writers create technical documentation, such as user manuals, white papers, and training materials. This course in 'Natural Language Processing in TensorFlow' can provide you with the skills to write clear and concise technical documentation. By learning NLP techniques, you'll gain a deeper understanding of how readers interact with text, enabling you to create more effective and user-friendly documentation.

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Digital Marketing Analyst

Digital Marketing Analysts analyze digital marketing campaigns and provide insights to improve their effectiveness. The 'Natural Language Processing in TensorFlow' course can equip you with the skills to analyze text data from social media, online reviews, and other digital channels. By understanding the sentiment and patterns within this data, you'll be able to make more informed decisions and optimize digital marketing campaigns.

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