Applied Deep Learning with TensorFlow and Google Cloud AI [Video]

Applied Deep Learning with TensorFlow and Google Cloud AI [Video]

Christian Fanli Ramsey, Haohan Wang

Take your Deep Learning skills to the next level using TensorFlow and Google Cloud AI
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Video Description

Deep Learning uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation on large volumes of data in order to make decisions about high dimensional data.

If you're looking to scale out your Deep Learning models and deploy your model into production then look no further because this video course will help you get the most out of TensorFlow and Keras to accelerate the training of your Deep Learning models and deploy your model at scale on the Cloud. Tools and frameworks such as TensorFlow, Keras, and Google Cloud MLE are used to showcase the strengths of various approaches, trade-offs, and building blocks for creating, training and evaluating your distributed deep learning models with GPU(s) and deploying your model to the Cloud. You will learn how to design and train your deep learning models and scale them out for larger datasets and complex neural network architectures on multiple GPUs using Google Cloud ML Engine. You’ll learn distributed techniques such as how parallelism and distribution work using low-level TensorFlow and high-level TensorFlow APIs and Keras.

Towards the end of the course, you will develop, train, and deploy your models using TensorFlow and Google Cloud Machine Learning Engine.

The code bundle for this video course is available at - https://github.com/PacktPublishing/Applied-Deep-Learning-with-TensorFlow-and-Google-Cloud-AI

Style and Approach

This video course adopts a tutorial-like approach to provide the right blend of theory,practical, and best practices in this rapidly developing area while providing a grounding in essential concepts that remain timeless and practical.

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What You Will Learn

  • Gain hands-on experience designing, training, and deploying your Deep Learning models with TensorFlow and Keras to handle large volumes of data and complex neural network architectures 
  • Get a better understanding of how parallelism and distribution work in TensorFlow and Keras
  • Design and experiment with complex neural network architectures using low-level TensorFlow while also using TensorFlow’s high level APIs and Keras 
  • Scale out training and prediction using different distributed techniques such as data parallelism using GPUs on our local machine and in the cloud using Google Cloud ML Engine
  • Develop, train, and deploy models using Google Cloud MLE to production.
  • Deploy your model as a production level API

Authors

Video Details

ISBN 139781788621601
Course Length5 hours 57 minutes
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