***NEW! Specialization Completion Challenge, receive Qwiklabs credits valued up to $150! See below for details.***
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
SPECIALIZATION COMPLETION CHALLENGE
As if learning new skills wasn’t enough of an incentive, we're excited to announce a special completion challenge for 'Machine Learning with TensorFlow on Google Cloud Platform’ specialization.
Here’s how it works: Our completion challenge runs through 11:59pm PT May 5, 2019. Complete any course in this Specialization including this one, anytime in this period and we'll send you 30 Qwiklabs credits for each course completed (upto $150 value given there are 5 courses in the specialization).
You can use these credits to take additional labs and earn badges, which you can then add to your resume and social profiles.
Your challenge awaits – begin learning on Coursera today!
Este curso forma parte de Programa Especializado - Machine Learning with TensorFlow on Google Cloud Platform
Intro to TensorFlow
ofrecido por
Acerca de este Curso
4.4
1,189 calificaciones
Habilidades que obtendrás
Application Programming Interfaces (API)EstimatorMachine LearningTensorflowCloud Computing
Programa - Qué aprenderás en este curso
Semana
17 minutos para completar
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 minutos)
3 horas para completar
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 minutos), 4 quizzes
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 programs16s
Lab Solution8m
Introduction5m
Shape problems3m
Fixing shape problems2m
Data type problems1m
Debugging full programs4m
Intro: Debugging full programs15s
Demo: Debugging Full Programs3m
3 ejercicios de práctica
What is TensorFlow?2m
Graphs and Sessions8m
Core TensorFlow20m
Semana
24 horas para completar
Estimator API
In this module we will walk you through the Estimator API.
18 videos (Total 67 minutos), 4 quizzes
18 videos
Estimator API3m
Pre-made Estimators5m
Demo: Housing Price Model1m
Checkpointing1m
Training on in-memory datasets2m
Lab Intro: Estimator API39s
Lab Solution: Estimator API10m
Train on large datasets with Dataset API8m
Lab Intro: Scaling up TensorFlow ingest using batching35s
Lab Solution: Scaling up TensorFlow ingest using batching5m
Big jobs, Distributed training6m
Monitoring with TensorBoard3m
Demo: TensorBoard UI28s
Serving Input Function5m
Recap: Estimator API1m
Lab Intro: Creating a distributed training TensorFlow model with Estimator API51s
Lab Solution: Creating a distributed training TensorFlow model with Estimator API7m
1 ejercicio de práctica
Estimator API18m
Semana
32 horas para completar
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 videos (Total 29 minutos), 2 quizzes
6 videos
Introduction31s
Why Cloud Machine Learning Engine?6m
Train a Model2m
Monitoring and Deploying Training Jobs2m
Lab Intro: Scaling TensorFlow with Cloud Machine Learning Engine50s
Lab Solution: Scaling TensorFlow with Cloud Machine Learning Engine16m
1 ejercicio de práctica
Cloud MLE10m
2 minutos para completar
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 minutos)
1 video
Summary2m
37%
comenzó una nueva carrera después de completar estos cursos
39%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
Principales revisiones
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.
Acerca de Google Cloud
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Acerca del programa especializado Machine Learning with TensorFlow on Google Cloud Platform
>>>Look for details below for COMPLETION CHALLENGE, receive up to $150 in Qwiklabs credits<<<
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.
Looking to make a career change? Upon completion of this Specialization, you’ll have the opportunity to share your information directly with Google and Publicis to be considered for open hiring opportunities.
COMPLETION CHALLENGE
For every course you complete before May 5, 2019, we will send you 30 Qwiklabs credits (upto $150 USD value)!
> 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 <
Preguntas Frecuentes
¿Puedo acceder a una vista preliminar del curso antes de inscribirme?
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¿Cuándo podré acceder a las lecciones y tareas?
Si decides inscribirte en el curso antes de la fecha de inicio de la sesión, tendrás acceso a todos los videos y las lecturas de la lección para el curso. Podrás enviar tareas en cuanto comience la sesión.
¿Qué recibiré cuando me inscriba?
Una vez que te inscribes y comienza la sesión, tendrás acceso a todos los videos y otros recursos, incluidos artículos de lectura y el foro de debate del curso. Podrás ver y enviar tareas de práctica y completar tareas con calificación obligatorias para obtener un título y un Certificado de curso
¿Cuándo recibiré mi Certificado de curso?
Si completas el curso de manera correcta, tu Certificado de curso electrónico se agregará a la página Logros. Desde allí, puedes imprimir tu Certificado de curso o agregarlo a tu perfil de LinkedIn
¿Por qué no puedo participar como oyente en este curso?
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¿Cuál es la política de reembolsos?
¿Hay ayuda económica disponible?
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