Transfer Learning for NLP with TensorFlow Hub

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在此免費指導項目中,您將:

Use pre-trained NLP text embedding models from TensorFlow Hub

Perform transfer learning to fine-tune models on real-world text data

Visualize model performance metrics with TensorBoard

在面試中展現此實踐經驗

Clock1.5 hours
Intermediate中級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

This is a hands-on project on transfer learning for natural language processing with TensorFlow and TF Hub. By the time you complete this project, you will be able to use pre-trained NLP text embedding models from TensorFlow Hub, perform transfer learning to fine-tune models on real-world data, build and evaluate multiple models for text classification with TensorFlow, and visualize model performance metrics with Tensorboard. Prerequisites: In order to successfully complete this project, you should be competent in the Python programming language, be familiar with deep learning for Natural Language Processing (NLP), and have trained models with TensorFlow or and its Keras API. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

必備條件

It is assumed that are competent in Python programming and have prior experience with building deep learning NLP models with TensorFlow or Keras

您要培養的技能

  • Natural Language Processing
  • Deep Learning
  • Inductive Transfer
  • Machine Learning
  • Tensorflow

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  1. Introduction and Project Overview

  2. Setup your TensorFlow and Colab GPU Runtime

  3. Download and Import the Quora Insincere Questions Dataset

  4. TensorFlow Hub for Natural Language Processing

  5. Define Function to Build Models

  6. Compile Models

  7. Train Various Text Classification Models

  8. Compare Accuracy and Loss Curves

  9. Fine-tune Model from TF Hub

  10. Train Bigger Models and Visualize Metrics with TensorBoard

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

授課教師

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