Learn Tensorflow from Pluralsight

Pluralsight offers you 29 courses, to learn Tensorflow now!

Pluralsight, is an American publicly held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 6,500 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.

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  • student Intermediate
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Build a Machine Learning Workflow with Keras TensorFlow 2.0

This course focuses on Keras as part of the TensorFlow 2.0 ecosystem^ including sequential APIs to build relatively straightforward models of stacked layers^ functional APIs for more complex models^ and model subclassing and custom layers.

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  • student Intermediate
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  • clock 1h 53m

Designing Data Pipelines with TensorFlow 2.0

This course will evaluate one of the largest changes from TensorFlow 1.0 to TensorFlow 2.0 – the tf.data module. This simplified and unified interface makes managing data pipelines easier with tf.data.

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  • student All Levels
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  • clock 3h 16m

End-to-End Machine Learning with TensorFlow on GCP

This course is set up as a workshop where you will do End-to-End Machine Learning with TensorFlow on Google Cloud Platform. It involves building an end-to-end model from data exploration all the way to deploying an ML model and getting …

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  • student Beginner
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  • clock 3h 9m

Getting Started with Tensorflow 2.0

This course focuses on introducing the TensorFlow 2.0 framework - exploring the features and functionality that it offers for building and training neural networks. This course discusses how TensorFlow 2.0 differs from TensorFlow 1.x and how the use of the …

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Introduction to TensorFlow

In this course^ you will learn how to create machine learning models in TensorFlow which is the tool we will use to write machine learning programs. You’ll learn how to use the TensorFlow libraries to solve numerical problems.

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  • student Intermediate
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  • clock 4h 46m

ng-conf 19: Introduction to Machine Learning with TensorFlow.js

Introduction to Machine Learning with TensorFlow.js | Asim Hussain

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  • student Intermediate
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  • clock 22m

Angular Denver 19: Machine Learning in Angular with TensorFlow.js

Angular Denver 2019 | Machine Learning in Angular with TensorFlow.js | Aaron Ma

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  • student Intermediate
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  • clock 2h 39m

Building Regression Models Using TensorFlow

Learn how the neurons in neural networks learn non-linear functions^ and how neural networks execute operations such as regression and classification in TensorFlow.

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  • student Intermediate
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  • clock 3h 2m

Building Unsupervised Learning Models with TensorFlow

Unsupervised learning techniques work with huge data sets to find patterns within the data. This course teaches you the details of clustering and autoencoding^ two versatile unsupervised learning techniques^ and how to implement them in TensorFlow.

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  • student Intermediate
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  • clock 2h 17m

Debugging and Monitoring TensorFlow Programs

This course goes deep into two specific tools in the TensorFlow toolkit - tfdbg and TensorBoard. These tools can be used to examine the internal state of TensorFlow programs and to visualize execution metrics and state.

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  • student Intermediate
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  • clock 2h 11m

Deploying TensorFlow Models to AWS^ Azure^ and the GCP

This course will show you how to take your TensorFlow model and deploy it locally or to the cloud platform of your choice- Azure^ AWS^ or the GCP.

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  • student All Levels
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  • clock 4h 20m

Image Understanding with TensorFlow on GCP

In this course^ we will take a look at different strategies for building an image classifier using convolutional neural networks. We ll improve the model s accuracy with augmentation^ feature extraction^ and fine-tuning hyperparameters while trying to avoid overfitting our …

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