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Natural Language Processing with TensorFlow: Teach language to machines using Python's deep learning library
Learning how to solve natural language processing (NLP) problems is an important skill to master due to the explosive growth of data combined with the demand for machine learning solutions in production. Natural Language Processing with TensorFlow, Second Edition, will teach you how to solve common real-world NLP problems with a variety of deep learning model architectures.
The book starts by getting readers familiar with NLP and the basics of TensorFlow. Then, it gradually teaches you different facets of TensorFlow 2.x. In the following chapters, you then learn how to generate powerful word vectors, classify text, generate new text, and generate image captions, among other exciting use-cases of real-world NLP.
TensorFlow has evolved to be an ecosystem that supports a machine learning workflow through ingesting and transforming data, building models, monitoring, and productionization. We will then read text directly from files and perform the required transformations through a TensorFlow data pipeline. We will also see how to use a versatile visualization tool known as TensorBoard to visualize our models.
By the end of this NLP book, you will be comfortable with using TensorFlow to build deep learning models with many different architectures, and efficiently ingest data using TensorFlow Additionally, you’ll be able to confidently use TensorFlow throughout your machine learning workflow.
The book starts by getting readers familiar with NLP and the basics of TensorFlow. Then, it gradually teaches you different facets of TensorFlow 2.x. In the following chapters, you then learn how to generate powerful word vectors, classify text, generate new text, and generate image captions, among other exciting use-cases of real-world NLP.
TensorFlow has evolved to be an ecosystem that supports a machine learning workflow through ingesting and transforming data, building models, monitoring, and productionization. We will then read text directly from files and perform the required transformations through a TensorFlow data pipeline. We will also see how to use a versatile visualization tool known as TensorBoard to visualize our models.
By the end of this NLP book, you will be comfortable with using TensorFlow to build deep learning models with many different architectures, and efficiently ingest data using TensorFlow Additionally, you’ll be able to confidently use TensorFlow throughout your machine learning workflow.
514 pages, ebook
Published July 1, 2022
20 people are currently reading
48 people want to read
About the author
Thushan Ganegedara
4 books3 followersThushan Ganegedara has completed his Ph.D. at the University of Sydney, Australia. He is specializing in machine learning and has a liking for deep learning. He lives dangerously and runs algorithms on untested data. He also works as the chief data scientist for AssessThreat, an Australian start-up. He got his BSc. (Hons) from the University of Moratuwa, Sri Lanka. He frequently writes technical articles and tutorials about machine learning. Additionally, he also strives for a healthy lifestyle by including swimming in his daily schedule.
🏅Top AI Writer | ML @ Canva | PhD👨🏾🎓.
Educator (Manning/Packt/DataCamp)
Youtube: @DeepLearningHero
LinkedIN: thushan.ganegedara
https://thushv89.medium.com/membership
🏅Top AI Writer | ML @ Canva | PhD👨🏾🎓.
Educator (Manning/Packt/DataCamp)
Youtube: @DeepLearningHero
LinkedIN: thushan.ganegedara
https://thushv89.medium.com/membership
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