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.
Ce cours fait partie de la Spécialisation Machine Learning with TensorFlow on Google Cloud Platform
Intro to TensorFlow
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À propos de ce cours
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Compétences que vous acquerrez
Application Programming Interfaces (API)EstimatorMachine LearningTensorflowCloud Computing
Résultats de carrière des étudiants
33%
ont commencé une nouvelle carrière après avoir terminé ce cours
38%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
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Cours 3 sur 5 dans le
Dates limites flexibles
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Niveau intermédiaire
Approx. 10 heures pour terminer
Recommandé : 2-3 weeks of study, 8-10 hours/week
Anglais
Sous-titres : Français, Portugais (brésilien), Allemand, Anglais, Espagnol, Japonais
Programme du cours : ce que vous apprendrez dans ce cours
7 minutes pour terminer
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 minutes pour terminer
2 vidéos (Total 7 min)
3 heures pour terminer
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.
3 heures pour terminer
19 vidéos (Total 72 min)
19 vidéos
Introduction1 min
What is TensorFlow2 min
Benefits of a Directed Graph5 min
TensorFlow API Hierarchy3 min
Lazy Evaluation4 min
Graph and Session4 min
Evaluating a Tensor2 min
Visualizing a graph2 min
Tensors6 min
Variables6 min
Lab Intro: Writing low-level TensorFlow programs16s
Lab Solution8 min
Introduction5 min
Shape problems3 min
Fixing shape problems2 min
Data type problems1 min
Debugging full programs4 min
Intro: Debugging full programs15s
Demo: Debugging Full Programs3 min
3 exercices pour s'entraîner
What is TensorFlow?2 min
Graphs and Sessions8 min
Core TensorFlow20 min
4 heures pour terminer
Estimator API
In this module we will walk you through the Estimator API.
4 heures pour terminer
18 vidéos (Total 67 min)
18 vidéos
Introduction1 min
Estimator API3 min
Pre-made Estimators5 min
Demo: Housing Price Model1 min
Checkpointing1 min
Training on in-memory datasets2 min
Lab Intro: Estimator API39s
Lab Solution: Estimator API10 min
Train on large datasets with Dataset API8 min
Lab Intro: Scaling up TensorFlow ingest using batching35s
Lab Solution: Scaling up TensorFlow ingest using batching5 min
Big jobs, Distributed training6 min
Monitoring with TensorBoard3 min
Demo: TensorBoard UI28s
Serving Input Function5 min
Recap: Estimator API1 min
Lab Intro: Creating a distributed training TensorFlow model with Estimator API51s
Lab Solution: Creating a distributed training TensorFlow model with Estimator API7 min
1 exercice pour s'entraîner
Estimator API18 min
2 heures pour terminer
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 heures pour terminer
6 vidéos (Total 29 min), 1 lecture, 2 quiz
6 vidéos
Introduction31s
Why Cloud AI Platform?6 min
Train a Model2 min
Monitoring and Deploying Training Jobs2 min
Lab Intro: Scaling TensorFlow with Cloud AI Platform50s
Lab Solution: Scaling TensorFlow with Cloud AI Platform16 min
1 lecture
Cloud ML Engine is now Cloud AI Platform10 min
1 exercice pour s'entraîner
Cloud AI Platform10 min
2 minutes pour terminer
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 minutes pour terminer
1 vidéo (Total 2 min)
1 vidéo
Summary2 min
Avis
4.5
Meilleurs avis pour INTRO TO TENSORFLOW
par LLAug 30, 2019
The tools and methods presented were great. The instructors were also fantastic.\n\nHowever the coding exercises were lacking in guidance even though the complete solution is given in the video.
par 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!
par YGApr 4, 2019
The procedure to connect to the cloud datalab was time consuming to do it every time.\n\nSuggestion : More topics in Core Tensorflow could be added. I enjoyed the course!
par SSJun 5, 2018
Nice introduce, might be more on introduce the model structure, because I still need to read additional notes to locate how to train my deep learning model online.
par MKMar 8, 2020
The course covers quite a few concepts -- TF basics, TF estimator, Google Cloud ML. It would be easier if the material is split into TF and Google Cloud lessons.
par TBOct 6, 2018
Great course as an introduction to TF, however, the labs are not as in depth as I'd have liked. Nonetheless, the course is well executed by the presenters.
par AHAug 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
par 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)
par MRJan 6, 2019
Excellent course!!! It is the best way to really understand TensorFlow and getting to know its real potential. Thanks to all the instructors!!!
par PSAug 5, 2019
Course is Best so far i learnt in Tensor Flow . It has all modules and content to be successful in Deep Learning and GCP Machine learning .
par PRDec 26, 2018
Amazing course! The short length of videos makes it lot easier for students to follow! Google is honestly the best at whatever it does! :)
par SRAug 2, 2018
Nice start. Have a look at Andrew NG's course, especially how they do the labs. Might give you some ideas for your next class versions.
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À propos du Spécialisation 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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