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Jan 1

Advanced Machine Learning with TensorFlow on Google Cloud Platform

January 1 @ 8:00 AM - 5:00 PM AEST

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Advanced Machine Learning with TensorFlow on Google Cloud Platform

This instructor led live training will give you hands-on experience optimizing, deploying, and scaling a variety of production ML models. You’ll learn how to build recommendation systems and scalable, accurate, and production-ready models for structured data, image data, time series, and natural language text.

What you Will learn

What's Included?

Instructor Live Training

An instructor will answer your questions

OFFICIAL GOOGLE CLOUD CONTENT

Course content reflects the latest google cloud class

hands on labs

Real world hands on labs provided by Qwiklabs and supported by instructor

CertIficate of completion

Receive official certificate on completion of 80% of labs

Who's this course for?

Level

Language

Duration

Prerequisites

Course Content

Topics

In the first course of this specialization, we recap what was covered in the Machine Learning with TensorFlow on Google Cloud Platform Specialization. One of the best ways to review something is to work with the concepts and technologies that you have learned. So, this course is run like a workshop where you will carry out end-to-end machine learning with TensorFlow on Google Cloud Platform. Here you will learn how to explore large datasets for features, create training and evaluation datasets, build models with the Estimator API in TensorFlow, train at scale and deploy those models into production with Google Cloud Platform machine learning tools.

New learners with ML background can also follow this course to learn how to do ML on GCP to fast track to the more advanced topics coming soon under the advanced specialization.

Topics

We’ll cover how to implement the various flavors of production ML systems—static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. We’ll delve into TensorFlow abstraction levels and the various options for doing distributed training and how to write distributed training models with custom estimators.

  • Compare static vs. dynamic training and inference
  • Manage model dependencies
  • Set up distributed training for fault tolerance, replication, and more
  • Export models for portability

Topics

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 avoiding overfitting our data.

  • Classify images using deep learning
  • Implement convolutional neural networks
  • Improve the model by augmentation, batch normalization, etc.
  • Leverage transfer learning

Objectives

  • Gain an overview of how ML is applied to image classification, including the evolving methods and challenges

Topics

  • Predict future values of a time-series
  • Classify free form text
  • Address time-series and text problems with recurrent neural networks
  • Choose between RNNs/LSTMs and simpler models
  • Train and reuse word embeddings in text problems

Objectives

This module is an introduction to sequence models and their applications, including an overview of sequence model architectures and how to handle inputs of variable length.

Topics

  • Devise a content-based recommendation engine
  • Implement a collaborative filtering recommendation engine
  • Build a hybrid recommendation engine with user and content embeddings

Objectives

Apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine.

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Have Questions?

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Ref: T-GCPAML-O-01

Details

Date:
January 1
Time:
8:00 AM - 5:00 PM AEST
Class Tags:

Location

Online

Instructor

Axalon Academy
Email:
training@axalon.io
View Instructor Website

Other

Competencies
Advanced
Learning Path
Data Scientist
Event Type
Live Virtual Training Day