This course covers designing and building a TensorFlow 2.x input data pipeline, building ML models with TensorFlow 2.x and Keras, improving the accuracy of ML models, writing ML models for scaled use and writing specialized ML models.
提供方
Google 云端平台课程信息
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100% 在线
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中级
完成时间大约为15 小时
英语（English）
字幕：英语（English）
您将学到的内容有
Create TensorFlow 2.x and Keras machine learning models and understand their key components
Use the tf.data library to manipulate data and large datasets
Use the Keras Sequential and Functional APIs for simple and advanced model creation
Train, deploy, and productionalize ML models at scale with Vertex AI
您将获得的技能
- Machine Learning
- Python Programming
- Build Input Data Pipeline
- Tensorflow
- keras
可灵活调整截止日期
根据您的日程表重置截止日期。
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。
中级
完成时间大约为15 小时
英语（English）
字幕：英语（English）
提供方
Google 云端平台
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
授课大纲 - 您将从这门课程中学到什么
完成时间为 1 分钟
Introduction to the Course
This module provides an overview of the course and its objectives.
完成时间为 1 分钟
1 个视频 （总计 1 分钟）
完成时间为 1 小时
Introduction to the TensorFlow ecosystem
This module introduces the TensorFlow framework and previews its main components as well as the overall API hierarchy.
完成时间为 1 小时
4 个视频 （总计 19 分钟）, 1 个阅读材料, 1 个测验
完成时间为 5 小时
Design and Build an Input Data Pipeline
Data is the a crucial component of a machine learning model. Collecting the right data is not enough. You also need to make sure you put the right processes in place to clean, analyze and transform the data, as needed, so that the model can take the most signal of it as possible. In this module we discuss training on large datasets with tf.data, working with in-memory files, and how to get the data ready for training. Then we discuss embeddings, and end with an overview of scaling data with tf.keras preprocessing layers.
完成时间为 5 小时
10 个视频 （总计 41 分钟）, 1 个阅读材料, 3 个测验
完成时间为 6 小时
Building Neural Networks with the TensorFlow and Keras API
In this module, we discuss activation functions and how they are needed to allow deep neural networks to capture nonlinearities of the data. We then provide an overview of Deep Neural Networks using the Keras Sequential and Functional APIs. Next we describe model subclassing, which offers greater flexibility in model building. The module ends with a lesson on regularization.
完成时间为 6 小时
11 个视频 （总计 45 分钟）, 1 个阅读材料, 4 个测验
完成时间为 2 小时
Training at Scale with Vertex AI
In this module, we describe how to train TensorFlow models at scale using Vertex AI.
完成时间为 2 小时
3 个视频 （总计 7 分钟）, 1 个阅读材料, 2 个测验
完成时间为 1 小时
Summary
This module is a summary of the TensorFlow on Google Cloud course.
完成时间为 1 小时
4 个阅读材料
审阅
- 5 stars61.81%
- 4 stars25.11%
- 3 stars8.97%
- 2 stars2.57%
- 1 star1.52%
来自TENSORFLOW ON GOOGLE CLOUD的热门评论
由 VC 提供May 17, 2020
I feel this course very valuable because it taught how to create an automated service in cloud with very huge data and working with distributed systems in production environment with minimal time.
由 AH 提供Aug 10, 2018
Pretty helpful in getting to know the various levels of abstractions of tensorflow API and avoiding various pitfalls while building the Tensorflow model
由 TK 提供Jun 16, 2018
The BEST course I've ever taken on TensorFlow! Definitely recommended to anyone interested in learning the fundamental of TF and GCP
由 YG 提供Apr 4, 2019
The procedure to connect to the cloud datalab was time consuming to do it every time. Suggestion : More topics in Core Tensorflow could be added. I enjoyed the course!
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