TensorFlow Tutorial 19 - Custom Dataset for Text with TextLineDataset faq

instructor Instructor: Aladdin Persson instructor-icon
duration Duration: 1.00 duration-icon

This tutorial provides an overview of how to create a custom dataset for text using TextLineDataset. It covers how to filter the dataset, create a vocabulary, numericalize with TokenTextEncoder, apply map on datasets, and create a simple model. It also covers how to use the dataset in several files. This tutorial provides a comprehensive guide to creating custom datasets for text.

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costCost:

Free

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Youtube

certificateCertificate:

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languageLanguage:

English

start dateStart Date:

On-Demand

Course Overview

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Updated in [February 21st, 2023]

This course provides an overview of how to use the TextLineDataset to create a custom dataset for text. It covers topics such as introduction and dataset overview, loading using TextLineDataset, filtering datasets, creating a vocabulary, numericalizing with TokenTextEncoder, applying map on datasets, creating a simple model, datasets in several files, sketch loading translation datasets, and ending. Participants will gain a better understanding of how to use the TextLineDataset to create custom datasets for text.

[Applications]
After completing this course, users can apply the knowledge they have gained to create custom datasets for text using TextLineDataset. They can use the TextLineDataset to filter datasets, create vocabularies, and numerically encode them with TokenTextEncoder. Additionally, users can apply map on datasets and create a simple model. Finally, users can also learn how to load translation datasets from several files.

[Career Paths]
1. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models. They use TensorFlow to build, train, and deploy models. They also use it to optimize existing models and develop new ones. With the increasing demand for machine learning applications, the demand for Machine Learning Engineers is also increasing.

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4. Deep Learning Engineer: Deep Learning Engineers use TensorFlow to develop and deploy deep learning models. They use it to build and train neural networks and develop algorithms to solve complex problems. With the increasing demand for deep learning applications, the demand for Deep Learning Engineers is also increasing.

[Education Paths]
1. Computer Science Degree: A computer science degree is a great way to learn the fundamentals of programming and software development. It covers topics such as algorithms, data structures, computer architecture, operating systems, and software engineering. With the rise of artificial intelligence and machine learning, computer science degrees are becoming increasingly popular and in demand.

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3. Data Science Degree: A data science degree focuses on the analysis and interpretation of data. It covers topics such as data mining, data visualization, machine learning, and predictive analytics. With the increasing demand for data-driven solutions, a data science degree is a great way to get ahead in the field.

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