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Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
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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...

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von

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Lehrplan - Was Sie in diesem Kurs lernen werden

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Woche
1

Woche 1

7 Minuten zum Abschließen

Introduction

7 Minuten zum Abschließen
2 Videos (Gesamt 7 min)
2 Videos
Intro to Qwiklabs5m
4 Stunden zum Abschließen

Core TensorFlow

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
Woche
2

Woche 2

5 Stunden zum Abschließen

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
Woche
3

Woche 3

2 Stunden zum Abschließen

Scaling TensorFlow models

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

2 Minuten zum Abschließen
1 Video (Gesamt 2 min)
1 Video

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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. > By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <...
Machine Learning with TensorFlow on Google Cloud Platform

Häufig gestellte Fragen

  • Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

  • If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

  • Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

  • If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

  • This course is one of a few offered on Coursera that are currently available only to learners who have paid or received financial aid, when available.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

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