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. Course Objectives: Create machine learning models in TensorFlow Use the TensorFlow libraries to solve numerical problems Troubleshoot and debug common TensorFlow code pitfalls Use tf.estimator to create, train, and evaluate an ML model Train, deploy, and productionalize ML models at scale with Cloud ML Engine
이 강좌는 Machine Learning with TensorFlow on Google Cloud Platform 전문 분야의 일부입니다.
Intro to TensorFlow
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About this Course
최근 조회 51,825회
귀하가 습득할 기술
Application Programming Interfaces (API)EstimatorMachine LearningTensorflowCloud Computing
강의 계획 - 이 강좌에서 배울 내용
주
1완료하는 데 7분 필요
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.
2 videos (Total 7 min)
완료하는 데 3시간 필요
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.
19 videos (Total 72 min), 4 quizzes
19개의 동영상
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
3개 연습문제
What is TensorFlow?2m
Graphs and Sessions8m
Core TensorFlow20m
주
2완료하는 데 4시간 필요
Estimator API
In this module we will walk you through the Estimator API.
18 videos (Total 67 min), 4 quizzes
18개의 동영상
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
1개 연습문제
Estimator API18m
주
3완료하는 데 2시간 필요
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.
6 videos (Total 29 min), 1 reading, 2 quizzes
6개의 동영상
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
1개의 읽기 자료
Cloud ML Engine is now Cloud AI Platform10m
1개 연습문제
Cloud AI Platform10m
완료하는 데 2분 필요
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.
1 video (Total 2 min)
1개의 동영상
Summary2m
4.4
159개의 리뷰35%
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Intro to TensorFlow의 최상위 리뷰
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!
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.
Google 클라우드 정보
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Machine Learning with TensorFlow on Google Cloud Platform 전문 분야 정보
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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