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.
Offered By
Google CloudAbout this Course
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100% online
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Intermediate Level
Approx. 15 hours to complete
English
Subtitles: English
What you will learn
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
Skills you will gain
- Machine Learning
- Python Programming
- Build Input Data Pipeline
- Tensorflow
- keras
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Intermediate Level
Approx. 15 hours to complete
English
Subtitles: English
Offered by
Google Cloud
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.
Syllabus - What you will learn from this course
1 minute to complete
Introduction to the Course
This module provides an overview of the course and its objectives.
1 minute to complete
1 video (Total 1 min)
1 hour to complete
Introduction to the TensorFlow ecosystem
This module introduces the TensorFlow framework and previews its main components as well as the overall API hierarchy.
1 hour to complete
4 videos (Total 19 min), 1 reading, 1 quiz
5 hours to complete
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.
5 hours to complete
10 videos (Total 41 min), 1 reading, 3 quizzes
6 hours to complete
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.
6 hours to complete
11 videos (Total 45 min), 1 reading, 4 quizzes
2 hours to complete
Training at Scale with Vertex AI
In this module, we describe how to train TensorFlow models at scale using Vertex AI.
2 hours to complete
3 videos (Total 7 min), 1 reading, 2 quizzes
1 hour to complete
Summary
This module is a summary of the TensorFlow on Google Cloud course.
1 hour to complete
4 readings
Reviews
- 5 stars61.78%
- 4 stars25.14%
- 3 stars9%
- 2 stars2.54%
- 1 star1.53%
TOP REVIEWS FROM TENSORFLOW ON GOOGLE CLOUD
by TKJun 16, 2018
The BEST course I've ever taken on TensorFlow! Definitely recommended to anyone interested in learning the fundamental of TF and GCP
by SRJun 14, 2018
very useful for getting up to speed on tensorflowa little inconvenient that you have to start the infrastructure for each lab, takes minutes (5-7)
by SKMay 10, 2020
A good understanding of bash cmds and a well-digested understanding of the course material is required to perform the labs. Quite challenging.
by DWOct 16, 2018
pretty good. some of the code in the last lab could be better explained. also please debug the cloud shell, as it does not always show the "web preview" button ;) otherwise, good job!
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