We introduce low-level TensorFlow and work our way through the necessary concepts and APIs so as to be able to write distributed machine learning models. Given a TensorFlow model, we explain how to scale out the training of that model and offer high-performance predictions using Cloud Machine Learning Engine.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Machine Learning with TensorFlow on Google Cloud Platform
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Über diesen Kurs
84,176 kürzliche Aufrufe
Karriereergebnisse der Lernenden
33%
nahm einen neuen Beruf nach Abschluss dieser Kurse auf
38%
ziehen Sie für Ihren Beruf greifbaren Nutzen aus diesem Kurs
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Stufe „Mittel“
Ca. 11 Stunden zum Abschließen
Englisch
Untertitel: Französisch, Portugiesisch (Brasilien), Deutsch, Englisch, Spanisch, Japanisch
Kompetenzen, die Sie erwerben
Application Programming Interfaces (API)EstimatorMachine LearningTensorflowCloud Computing
Karriereergebnisse der Lernenden
33%
nahm einen neuen Beruf nach Abschluss dieser Kurse auf
38%
ziehen Sie für Ihren Beruf greifbaren Nutzen aus diesem Kurs
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Stufe „Mittel“
Ca. 11 Stunden zum Abschließen
Englisch
Untertitel: Französisch, Portugiesisch (Brasilien), Deutsch, Englisch, Spanisch, Japanisch
von
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.
Lehrplan - Was Sie in diesem Kurs lernen werden
7 Minuten zum Abschließen
Introduction
The tool we will use to write machine learning programs is TensorFlow and so in this course, we will introduce you to TensorFlow. In the first course, you learned how to formulate business problems as machine learning problems and in the second course, you learned how machine works in practice and how to create datasets that you can use for machine learning. Now that you have the data in place, you are ready to get started writing machine learning programs.
7 Minuten zum Abschließen
2 Videos (Gesamt 7 min)
4 Stunden zum Abschließen
Core TensorFlow
We will introduce you to the core components of TensorFlow and you will get hands-on practice building machine learning programs. You will compare and write lazy evaluation and imperative programs, work with graphs, sessions, variables, as finally debug TensorFlow programs.
4 Stunden zum Abschließen
19 Videos (Gesamt 72 min)
19 Videos
What is TensorFlow2m
Benefits of a Directed Graph5m
TensorFlow API Hierarchy3m
Lazy Evaluation4m
Graph and Session4m
Evaluating a Tensor2m
Visualizing a graph2m
Tensors6m
Variables6m
Lab Intro: Writing low-level TensorFlow programs16
Lab Solution8m
Introduction5m
Shape problems3m
Fixing shape problems2m
Data type problems1m
Debugging full programs4m
Intro: Debugging full programs15
Demo: Debugging Full Programs3m
3 praktische Übungen
What is TensorFlow?30m
Graphs and Sessions30m
Core TensorFlow30m
5 Stunden zum Abschließen
Estimator API
In this module we will walk you through the Estimator API.
5 Stunden zum Abschließen
18 Videos (Gesamt 67 min)
18 Videos
Estimator API3m
Pre-made Estimators5m
Demo: Housing Price Model1m
Checkpointing1m
Training on in-memory datasets2m
Lab Intro: Estimator API39
Lab Solution: Estimator API10m
Train on large datasets with Dataset API8m
Lab Intro: Scaling up TensorFlow ingest using batching35
Lab Solution: Scaling up TensorFlow ingest using batching5m
Big jobs, Distributed training6m
Monitoring with TensorBoard3m
Demo: TensorBoard UI28
Serving Input Function5m
Recap: Estimator API1m
Lab Intro: Creating a distributed training TensorFlow model with Estimator API51
Lab Solution: Creating a distributed training TensorFlow model with Estimator API7m
1 praktische Übung
Estimator API30m
2 Stunden zum Abschließen
Scaling TensorFlow models
I’m here to talk about how you would go about taking your TensorFlow model and training it on GCP’s managed infrastructure for machine learning model training and deployed.
2 Stunden zum Abschließen
6 Videos (Gesamt 29 min), 1 Lektüre, 2 Quiz
6 Videos
Why Cloud AI Platform?6m
Train a Model2m
Monitoring and Deploying Training Jobs2m
Lab Intro: Scaling TensorFlow with Cloud AI Platform50
Lab Solution: Scaling TensorFlow with Cloud AI Platform16m
1 Lektüre
Cloud ML Engine is now Cloud AI Platform10m
1 praktische Übung
Cloud AI Platform30m
2 Minuten zum Abschließen
Summary
Here we summarize the TensorFlow topics we covered so far in this course. We'll revisit core TensorFlow code, the Estimator API, and end with scaling your machine learning models with Cloud Machine Learning Engine.
2 Minuten zum Abschließen
1 Video (Gesamt 2 min)
1 Video
Summary2m
Bewertungen
Top-Bewertungen von INTRO TO TENSORFLOW
von VC17. Mai 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.
von LL30. Aug. 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.
von DW16. Okt. 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!
von TW30. März 2020
This needs some updating - looks like the Tensorboard is no longer accessed in the same way as it was when this course was produced. Otherwise great! Good challenges! :/
Über den Spezialisierung Machine Learning with TensorFlow on Google Cloud Platform
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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