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.

Transfer Learning for NLP with TensorFlow Hub

位教师:Snehan Kekre
访问权限由 Coursera Learning Team 提供
12,392 人已注册
您将学到什么
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
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要了解的详细信息

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在 2 小时内学习、练习并应用岗位必备技能
- 接受行业专家的培训
- 获得解决实训工作任务的实践经验
- 使用最新的工具和技术来建立信心

关于此指导项目
分步进行学习
在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:
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Introduction to the Project (9 min)
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Setup your TensorFlow and Colab Runtime (10 min)
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Download and Import the Quora Insincere Questions Dataset (13 min)
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TensorFlow Hub for Natural Language Processing (11 min)
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Define Function to Build and Compile Models (9 min)
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Define Function to Compile and Train Models (8 min)
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Train Various Text Classification Models (6 min)
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Compare Accuracy and Loss Curves (4 min)
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Fine-tuning Models from TF Hub (3 min)
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Train Bigger Models and Visualize Metrics with TensorBoard (7 min)
推荐体验
It is assumed that are competent in Python programming and have prior experience with building deep learning NLP models with TensorFlow or Keras
7个项目图片
位教师

提供方
学习方式
基于技能的实践学习
通过完成与工作相关的任务来练习新技能。
专家指导
使用独特的并排界面,按照预先录制的专家视频操作。
无需下载或安装
在预配置的云工作空间中访问所需的工具和资源。
仅在台式计算机上可用
此指导项目专为具有可靠互联网连接的笔记本电脑或台式计算机而设计,而不是移动设备。
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- 5 stars
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显示 3/181 个
已于 Jul 24, 2021审阅
This is definitely a good project for TensorFlow tech learning. The instructor was very inspired.
已于 Aug 8, 2021审阅
Highly recommended for those who want to gain upper hand in NLP tasks through modern transfer learning techniques
已于 Sep 30, 2020审阅
Nice project ... Make a tutorial for neural style transfer with different type of GAN...






