Machine Learning with TensorFlow on Google Cloud

GCP-110 (5 Days)
Request Pricing for Machine Learning with TensorFlow on Google Cloud

Google Cloud Training Overview

This Machine Learning with TensorFlow on Google Cloud training course teaches attendees how to write distributed machine learning (ML) models that scale in TensorFlow. Students learn how to incorporate the right mix of parameters to yield accurate, generalized models to solve specific types of ML problems.

Location and Pricing

This course is taught as a private online class for teams of 3 or more. All our private online courses are hands-on, instructor-led, and customizable to fit your group’s goals and needs. To receive a customized proposal and price quote, please contact us.

Google Cloud Training Objectives

All students will learn how to:

  • Frame a business use case as a machine learning problem
  • Create machine learning datasets that are capable of achieving generalization
  • Implement machine learning models using TensorFlow
  • Understand the impact of gradient descent parameters on accuracy, training speed, sparsity, and generalization
  • Build and operationalize distributed TensorFlow models
  • Represent and transform features

Google Cloud Training Outline

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Introduction
How Google Does Machine Learning
  • Develop a data strategy around machine learning
  • Examine use cases that are then reimagined through an ML lens
  • Recognize biases that ML can amplify
  • Leverage Google Cloud Platform tools and environment to do ML
  • Learn from Google's experience to avoid common pitfalls
  • Carry out data science tasks in online collaborative notebooks
  • Invoke pre-trained ML models from Cloud Datalab
Launching into Machine Learning
  • Identify why deep learning is currently popular
  • Optimize and evaluate models using loss functions and performance metrics
  • Mitigate common problems that arise in machine learning
  • Create repeatable and scalable training, evaluation, and test datasets
Intro to TensorFlow
  • 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
Feature Engineering
  • Turn raw data into feature vectors
  • Preprocess and create new feature pipelines with Cloud Dataflow
  • Create and implement feature crosses and assess their impact
  • Write TensorFlow Transform code for feature engineering
The Art and Science of ML
  • Optimize model performance with hyperparameter tuning
  • Experiment with neural networks and fine-tune performance
  • Enhance ML model features with embedding layers
  • Create reusable custom model code with the Custom Estimator
Conclusion
Request Pricing for Machine Learning with TensorFlow on Google Cloud
Lecture percentage

50%

Lecture/Demo

Lab percentage

50%

Lab

Course Number:

GCP-110

Duration:

5 Days

Prerequisites:

All students should have:

  • Experience coding in Python
  • Knowledge of basic statistics
  • Knowledge of SQL and cloud computing (helpful)

Training Materials:

All GCP training students receive comprehensive courseware.

Software Requirements:

Students must have a modern web browser (ideally Chrome) and Internet access

Contact Us:

Accelebrate’s training classes are available for private groups of 3 or more people at your site or online anywhere worldwide.

Don't settle for a "one size fits all" public class! Have Accelebrate deliver exactly the training you want, privately at your site or online, for less than the cost of a public class.

For pricing and to learn more, please contact us.

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