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

More Information
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
About

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.

Features
  • Cover the fundamental concepts of Deep Learning 
  • Design your model from data ingestion to deployment at scale
  • Use distributed techniques using TensorFlow and deploy your model with Google Cloud MLE.
Course Length 5 hours 57 minutes
ISBN9781788621601
Date Of Publication 30 Jul 2018

Authors

Haohan Wang

Haohan Wang is a deep learning researcher. Her focus is using machine learning to process psychophysiological data to understand people’s emotions and mood states to provide support for people’s well-being. She has a background in statistics and finance and has continued her studies in deep learning and neurobiology.

Christian and Haohan together they make dyadxmachina and their focus area is at the interaction of deep learning and psychophysiology, which means they mainly focus on 2 areas:

  • They want to help further intelligent systems to understand emotions and mood states of their users so they can react accordingly
  • They also want to help people understand their emotions, stress responses, mood states and how they vary over time in order to help people become more emotionally aware and resilient

Christian Fanli Ramsey

Christian Fanli Ramsey is an applied data scientist at IDEO. He is currently working at Greenfield Labs a research center between IDEO and Ford that focuses on the future of mobility. His primary focus on understanding complex emotions, stress levels and responses by using deep learning and machine learning to measure and classify psychophysiological signals.

Christian and Haohan together they make dyadxmachina and their focus area is at the interaction of deep learning and psychophysiology, which means they mainly focus on 2 areas:

  • They want to help further intelligent systems to understand emotions and mood states of their users so they can react accordingly
  • They also want to help people understand their emotions, stress responses, mood states and how they vary over time in order to help people become more emotionally aware and resilient