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
Este curso faz parte do Programa de cursos integrados Machine Learning with TensorFlow on Google Cloud Platform
Intro to TensorFlow
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Informações sobre o curso
68,843
68,843Habilidades que você terá
Application Programming Interfaces (API)EstimatorMachine LearningTensorflowCloud Computing
Programa - O que você aprenderá com este curso
Semana
17 minutos para concluir
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 vídeos (total de (Total 7 mín.) min)
3 horas para concluir
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 vídeos (total de (Total 72 mín.) min), 4 testes
19 videos
Introduction1min
What is TensorFlow2min
Benefits of a Directed Graph5min
TensorFlow API Hierarchy3min
Lazy Evaluation4min
Graph and Session4min
Evaluating a Tensor2min
Visualizing a graph2min
Tensors6min
Variables6min
Lab Intro: Writing low-level TensorFlow programs16s
Lab Solution8min
Introduction5min
Shape problems3min
Fixing shape problems2min
Data type problems1min
Debugging full programs4min
Intro: Debugging full programs15s
Demo: Debugging Full Programs3min
3 exercícios práticos
What is TensorFlow?2min
Graphs and Sessions8min
Core TensorFlow20min
Semana
24 horas para concluir
Estimator API
In this module we will walk you through the Estimator API.
18 vídeos (total de (Total 67 mín.) min), 4 testes
18 videos
Introduction1min
Estimator API3min
Pre-made Estimators5min
Demo: Housing Price Model1min
Checkpointing1min
Training on in-memory datasets2min
Lab Intro: Estimator API39s
Lab Solution: Estimator API10min
Train on large datasets with Dataset API8min
Lab Intro: Scaling up TensorFlow ingest using batching35s
Lab Solution: Scaling up TensorFlow ingest using batching5min
Big jobs, Distributed training6min
Monitoring with TensorBoard3min
Demo: TensorBoard UI28s
Serving Input Function5min
Recap: Estimator API1min
Lab Intro: Creating a distributed training TensorFlow model with Estimator API51s
Lab Solution: Creating a distributed training TensorFlow model with Estimator API7min
1 exercício prático
Estimator API18min
Semana
32 horas para concluir
Scaling TensorFlow models with CMLE
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 vídeos (total de (Total 29 mín.) min), 2 testes
6 videos
Introduction31s
Why Cloud Machine Learning Engine?6min
Train a Model2min
Monitoring and Deploying Training Jobs2min
Lab Intro: Scaling TensorFlow with Cloud Machine Learning Engine50s
Lab Solution: Scaling TensorFlow with Cloud Machine Learning Engine16min
1 exercício prático
Cloud MLE10min
2 minutos para concluir
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 vídeo (total de (Total 2 mín.) min)
1 vídeos
Summary2min
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39%
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Melhores avaliações
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.
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Sobre o Programa de cursos integrados 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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