Transfer Learning for NLP with TensorFlow Hub

Transfer Learning for NLP with TensorFlow Hub

Transfer Learning for NLP with TensorFlow Hub Taught in English Instructor: Snehan Kekre 9,409 already enrolled Included with • Learn more Guided Project Learn, practice, and apply job-ready skills with expert guidance Intermediate level Recommended experience Close Recommended experience Intermediate level It is assumed that are competent in Python programming and have prior experience with

Description

Transfer learning has become an increasingly popular technique in the field of natural language processing (NLP) due to its ability to leverage pre-trained models and adapt them to new tasks with minimal data and computational resources. One such tool that has gained prominence in the NLP community is TensorFlow Hub, a repository of reusable machine learning modules.

TensorFlow Hub provides a wide range of pre-trained models that can be easily integrated into TensorFlow-based projects. These models have been trained on large datasets and have already learned to extract useful features from text data. By utilizing transfer learning with TensorFlow Hub, developers can save time and effort in training new models from scratch, especially when working with limited data.

One of the key advantages of transfer learning with TensorFlow Hub is its versatility. The platform offers a variety of pre-trained

Transfer Learning for NLP with TensorFlow Hub

Taught in English

Snehan Kekre

Instructor: Snehan Kekre

9,409 already enrolled

Included with Coursera Plus

Guided Project

Learn, practice, and apply job-ready skills with expert guidance

Intermediate level

Recommended experience

1.5 hours
Learn at your own pace
No downloads or installation required
Only available on desktop
Hands-on learning
4.8

(168 reviews)

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