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第 1 门课程(共 3 门)
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中级
完成时间大约为26 小时
英语(English)
字幕:英语(English)
可分享的证书
完成后获得证书
100% 在线
立即开始,按照自己的计划学习。
第 1 门课程(共 3 门)
可灵活调整截止日期
根据您的日程表重置截止日期。
中级
完成时间大约为26 小时
英语(English)
字幕:英语(English)

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伦敦帝国学院

教学大纲 - 您将从这门课程中学到什么

1

1

完成时间为 3 小时

Introduction to TensorFlow

完成时间为 3 小时
14 个视频 (总计 59 分钟), 8 个阅读材料
14 个视频
Welcome to week 11分钟
Hello TensorFlow!1分钟
[Coding tutorial] Hello TensorFlow!2分钟
What's new in TensorFlow 24分钟
Interview with Laurence Moroney5分钟
Introduction to Google Colab2分钟
[Coding tutorial] Introduction to Google Colab8分钟
TensorFlow documentation3分钟
TensorFlow installation3分钟
[Coding tutorial] pip installation3分钟
[Coding tutorial] Running TensorFlow with Docker10分钟
Upgrading from TensorFlow 13分钟
[Coding tutorial] Upgrading from TensorFlow 16分钟
8 个阅读材料
About Imperial College & the team10分钟
How to be successful in this course10分钟
Grading policy10分钟
Additional readings & helpful references10分钟
What is TensorFlow?10分钟
Google Colab resources10分钟
TensorFlow documentation10分钟
Upgrade TensorFlow 1.x Notebooks10分钟
2

2

完成时间为 7 小时

The Sequential model API

完成时间为 7 小时
13 个视频 (总计 59 分钟)
13 个视频
What is Keras?1分钟
Building a Sequential model4分钟
[Coding tutorial] Building a Sequential model4分钟
Convolutional and pooling layers4分钟
[Coding tutorial] Convolutional and pooling layers5分钟
The compile method5分钟
[Coding tutorial] The compile method5分钟
The fit method4分钟
[Coding tutorial] The fit method7分钟
The evaluate and predict methods6分钟
[Coding tutorial] The evaluate and predict methods4分钟
Wrap up and introduction to the programming assignment1分钟
2 个练习
[Knowledge check] Feedforward and convolutional neural networks15分钟
[Knowledge check] Optimisers, loss functions and metrics15分钟
3

3

完成时间为 7 小时

Validation, regularisation and callbacks

完成时间为 7 小时
11 个视频 (总计 60 分钟)
11 个视频
Interview with Andrew Ng6分钟
Validation sets4分钟
[Coding Tutorial] Validation sets9分钟
Model regularisation6分钟
[Coding Tutorial] Model regularisation4分钟
Introduction to callbacks5分钟
[Coding tutorial] Introduction to callbacks7分钟
Early stopping and patience6分钟
[Coding tutorial] Early stopping and patience5分钟
Wrap up and introduction to the programming assignment51
1 个练习
[Knowledge check] Validation and regularisation15分钟
4

4

完成时间为 7 小时

Saving and loading models

完成时间为 7 小时
12 个视频 (总计 74 分钟)
12 个视频
Saving and loading model weights6分钟
[Coding tutorial] Saving and loading model weights10分钟
Model saving criteria4分钟
[Coding tutorial] Model saving criteria11分钟
Saving the entire model4分钟
[Coding tutorial] Saving the entire model8分钟
Loading pre-trained Keras models5分钟
[Coding tutorial] Loading pre-trained Keras models7分钟
TensorFlow Hub modules2分钟
[Coding tutorial] TensorFlow Hub modules8分钟
Wrap up and introduction to the programming assignment1分钟

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关于 TensorFlow 2 for Deep Learning 专项课程

This Specialization is intended for machine learning researchers and practitioners who are seeking to develop practical skills in the popular deep learning framework TensorFlow. The first course of this Specialization will guide you through the fundamental concepts required to successfully build, train, evaluate and make predictions from deep learning models, validating your models and including regularisation, implementing callbacks, and saving and loading models. The second course will deepen your knowledge and skills with TensorFlow, in order to develop fully customised deep learning models and workflows for any application. You will use lower level APIs in TensorFlow to develop complex model architectures, fully customised layers, and a flexible data workflow. You will also expand your knowledge of the TensorFlow APIs to include sequence models. The final course specialises in the increasingly important probabilistic approach to deep learning. You will learn how to develop probabilistic models with TensorFlow, making particular use of the TensorFlow Probability library, which is designed to make it easy to combine probabilistic models with deep learning. As such, this course can also be viewed as an introduction to the TensorFlow Probability library. Prerequisite knowledge for this Specialization is python 3, general machine learning and deep learning concepts, and a solid foundation in probability and statistics (especially for course 3)....
TensorFlow 2 for Deep Learning

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