This course is focused on using the flexibility and “ease of use” of TensorFlow 2.x and Keras to build, train, and deploy machine learning models. You will learn about the TensorFlow 2.x API hierarchy and will get to know the main components of TensorFlow through hands-on exercises. We will introduce you to working with datasets and feature columns. You will learn how to design and build a TensorFlow 2.x input data pipeline. You will get hands-on practice loading csv data, numPy arrays, text data, and images using tf.Data.Dataset. You will also get hands-on practice creating numeric, categorical, bucketized, and hashed feature columns.
This course is part of the Machine Learning with TensorFlow on Google Cloud Platform Specialization
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About this Course
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Learner Career Outcomes
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Intermediate Level
Approx. 19 hours to complete
English
Subtitles: French, Portuguese (European), Russian, English, Spanish
What you will learn
Use the Keras Sequential and Functional APIs for simple and advanced model creation
Design and build a TensorFlow 2.x input data pipeline
Use the tf.data library to manipulate data and large datasets
Train, deploy, and productionalize ML models at scale with Cloud AI Platform
Skills you will gain
Machine LearningPython ProgrammingBuild Input Data PipelineTensorflowkeras
Learner Career Outcomes
25%
started a new career after completing these courses
31%
got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 3 of 5 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 19 hours to complete
English
Subtitles: French, Portuguese (European), Russian, English, Spanish
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
7 minutes to complete
Introduction to course
This course is an introduction to TensorFlow 2.x, which incorporates the ease of use of Keras for building machine learning models. This course covers designing and building a TensorFlow 2.x input data pipeline, building machine learning models with TensorFlow 2.x and Keras, improving the accuracy of machine learning models, and writing machine learning models for scaled use.
7 minutes to complete
2 videos (Total 7 min)
3 hours to complete
Introduction to TensorFlow
We will introduce you to the new paradigm of TensorFlow 2.x. You will learn about the the TensorFlow API hierarchy and will get to know the main components of TensorFlow, tensors and variables, through hands-on exercises.
3 hours to complete
5 videos (Total 22 min), 1 reading, 5 quizzes
5 videos
TensorFlow API Hierarchy4m
Components of TensorFlow: Tensors and Variables8m
Lab Intro Introduction to Tensors and Variables1m
Lab Intro Writing low-level TensorFlow programs43s
1 reading
Readings10m
3 practice exercises
Introduction to TensorFlow15m
API Hierarchy15m
Tensors and Variables15m
8 hours to complete
Design and Build a TensorFlow Input Data Pipeline
We will introduce you to working with datasets and feature columns. You will get hands-on practice loading csv data, numPy arrays with tf.data.Dataset, text data, and load images using tf.data.Dataset. You will also get hands on practice creating numeric, categorical, bucketized, and hashed feature columns.
8 hours to complete
10 videos (Total 25 min), 1 reading, 9 quizzes
10 videos
Overview4m
Working in-memory and with files3m
Getting the data ready for model training6m
Lab Intro Load CSV and Numpy Data 28s
Lab Intro Loading Image Data54s
Lab Intro Feature Columns37s
Optional Lab Intro TFRecord and tf.Example1m
Training on Large Datasets with tf.data API4m
Lab Intro Manipulating data with Tensorflow Dataset API34s
Optional Lab Intro Feature Analysis Using TensorFlow Data Validation and Facets1m
1 reading
Readings15m
3 practice exercises
PRACTICE QUIZ: Getting the data ready for model training15m
Training on Large Datasets with tf.data API15m
Design and Build Input Data Pipeline15m
4 hours to complete
Training neural networks with Tensorflow 2 and the Keras Sequential API
In this module you will get introduced to writing TensorFlow models using the Keras Sequential API. But, before you jump right into writing the model, we will talk about activation functions, loss and optimization. You will then be introduced to the Keras Sequential API to learn how you can create deep learning models with it. You will also learn how to deploy the model for prediction in the cloud.
4 hours to complete
5 videos (Total 23 min), 1 reading, 5 quizzes
5 videos
Overview31s
Activation functions8m
Activation functions: Pitfalls to avoid in Backpropagation 5m
Neural Networks with Keras Sequential API7m
Lab intro Keras Sequential API21s
1 reading
Readings10m
2 practice exercises
Activation Functions15m
Neural Networks with TF2 and Keras15m
3 hours to complete
Training neural networks with Tensorflow 2 and the Keras Functional API
The Sequential model API is great for developing deep learning models in most situations, but it also has some limitations. One example is that it is not straightforward to define models that may have multiple different input sources, produce multiple output destinations or models that re-use layers. The Keras Functional API is a way to create models that are more flexible than the tf.keras.Sequential API. The functional API can handle models with non-linear topology, models with shared layers, and models with multiple inputs or outputs. The Keras Functional API provides a more flexible way for defining models. It specifically allows you to define multiple input or output models as well as models that share layers. More than that, it allows you to define ad hoc acyclic network graphs. The main idea that a deep learning model is usually a directed acyclic graph (DAG) of layers. So the Functional API is a way to build graphs of layers. Additionally, we will also talk about how regularization can help with model performance.
3 hours to complete
6 videos (Total 29 min), 1 reading, 4 quizzes
6 videos
Regularization: The Basics4m
Regularization: L1, L2, and Early Stopping5m
Regularization: Dropout5m
Serving models in the Cloud3m
Lab intro Keras Functional API38s
1 reading
Readings1h
3 practice exercises
The Keras Functional API15m
Regularization15m
Serving Models in the Cloud15m
1 hour to complete
Summary
Here we summarize the TensorFlow topics we covered so far in this course. We'll revisit core TensorFlow code, the tf.data API, Keras Sequential and Functional APIs and end with scaling your machine learning models with Cloud AI Platform.
1 hour to complete
1 video (Total 8 min), 1 reading, 1 quiz
1 video
1 reading
Quiz Questions to ALL Lessons 10m
1 practice exercise
Course Summary15m
Reviews
TOP REVIEWS FROM INTRODUCTION TO TENSORFLOW
by AJNov 14, 2020
Excellent 'Introduction' to TensorFlow 2.0 (HINT: 'Introduction' does not mean 'Easy'). Evan Jones is at his best giving rapid intuitive explanations of advanced topics in deep neural networks.
by VCMay 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.
by LLAug 30, 2019
The tools and methods presented were great. The instructors were also fantastic. However the coding exercises were lacking in guidance even though the complete solution is given in the video.
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!
About the Machine Learning with TensorFlow on Google Cloud Platform Specialization
What is machine learning, and what kinds of problems can it solve? What are the five phases of converting a candidate use case to be driven by machine learning, and why is it important that the phases not be skipped? Why are neural networks so popular now? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent and a thoughtful way of creating datasets? Learn how to write distributed machine learning models that scale in Tensorflow, scale out the training of those models. and offer high-performance predictions. Convert raw data to features in a way that allows ML to learn important characteristics from the data and bring human insight to bear on the problem. Finally, learn how to incorporate the right mix of parameters that yields accurate, generalized models and knowledge of the theory to solve specific types of ML problems. You will experiment with end-to-end ML, starting from building an ML-focused strategy and progressing into model training, optimization, and productionalization with hands-on labs using Google Cloud Platform.
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