Intro to TensorFlow

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 feature

( 108 Reviews )

Created by: Google Cloud Training

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Overall Score : 80 / 100

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Course Description

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 TensorFlowUse the TensorFlow libraries to solve numerical problemsTroubleshoot and debug common TensorFlow code pitfallsUse tf.estimator to create, train, and evaluate an ML modelTrain, deploy, and productionalize ML models at scale with Cloud ML EngineCOMPLETION CHALLENGEComplete any GCP specialization from November 5 - November 30, 2019 for an opportunity to receive a GCP t-shirt (while supplies last). Check Discussion Forums for details.

Time: 10h

Instructor Details

Google Cloud Training

4.0 Rating
108 Reviews

The Google Cloud Training team is responsible for developing, delivering and evaluating training that enables our enterprise customers and partners to use our products and solution offerings in an effective and impactful way. Google Cloud helps millions of organizations empower their employees, serve their customers, and build what's next for their businesses with innovative technology created in-and for-the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.

Reviews

4.0

108 total reviews

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By nicolas j on 8-Dec-18

/!\ THIS COURSE IS A FRAUD /!\this course is a way more about google cloud than tensorflow:-You'll never learn how to install and setup tensorflow on your own infrastructure. Count how many time the logo of google cloud vs tensorflow appears. Another fact? they propose to win a google cloud tshirt on the forum... but you'll learn how to use google cloud...-Tutors tell to don't use the forum on coursera, but the google tickets system... but as long as I pay on coursera, I expect to have a serious following from the tutors on the coursera forum, isn't it legit ?-Of course they don't forget to send you email about google cloud products...-You only have to pass quiz, no need to do the exercises, just log to them then your good to pass... is that serious ? what does worth my certificate ?-I'm definitely not happy of this one, this will down my confidence in coursera...btw I will send this message to coursera either in the way to get some explanation how this could be possible... I mean we are talking about google doing a fraud!

By MIchael on 23-Jan-19

even though the instructors present in a great way, especially the labs seem to be quite confusing and the videos couldn't prepare me so well for what expects me in the lab

By Nithin S on 10-Jan-19

While it is a fairly basic and informative course, I could was left disappointed with a few thingsI found the lab infrastructure hard to setup and of limited use.The lab exercise were trivial and not up to markThe lab was not graded or no scope for us to run them independently on our cluster on our own cloud.I was slightly irritated with trainers trying to promote google product instead of focusing on technical training.Google BigQuery was an unnecessary addition and distracting till you realize you don't need it for this training.

By Sudesh A on 14-Jul-18

The introduction to TensorFlow was good. Lab needs improvement; it would be helpful to have code templates that needs to be filled in by us to get credit for the lab, instead of just executing the code. Content in Estimator API module needs a bit more depth/explanation in my opinion.

By Juan M P on 31-Jan-19

Although the videos and content in general is OK, the environment and setup of Google DataLab for each lab is really disgusting takes about 10 minutes to start with the proper exercise.I understand that Google wants us to use their products, but the main purpose of this course (learning TensorFlow) is cluttered with this environment.

By Miika M on 21-Nov-18

Don't think I'll remember much of what I've seen two weeks from now. Most of the time in the course was spent on spinning up the google cloud stuff. All the labs are done for you so no need to use your own brain. I'm very disappointed.

By George on 6-Aug-18

labs were not properly working...

By john f d on 18-Jul-18

Labs vms are to slow. Speaker is difficult to understand. Mic varies and speech pattern is not clear. The presentations need some graphics rather than a guy talking. Sketch out the ideas on a white board rather than talking 5 minutes to a single slide.

By Raghuram N on 29-Apr-19

Good introductory course on Tensorflow.

By Aditya h on 11-Aug-18

Pretty helpful in getting to know the various levels of abstractions of tensorflow API and avoiding various pitfalls while building the Tensorflow model

By Marc M v W on 21-Jan-19

Sadly disappointing. I was hoping for a more detailed presentation of TensorFlow and its capacities. But no words about the Keras level, TensorHub, and other TensorFlow really useful parts. Instead, we have a gentle "hello world" type of introduction to TensorFlow low level and estimation layers, and how to deploy an application to Google Cloud. Now it's not bad, just disappointing.

By Richard K on 26-Jan-19

The course could have more programming components.

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