Intro To TensorFlow: Machine Learning With TensorFlow On Google Cloud Platform Specialization

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About Intro to TensorFlow

In this course you get introduced to low-level TensorFlow and do your work through essential concepts and APIs that enable us to write distributed machine learning models. Looking at a TensorFlow model, you can understand how to scale the training of that model and predict high-performance using a cloud machine learning engine. This Intro to Tensorflow, Machine Learning Courses by Google can be completed within 3 weeks in about 11 hours of study.

Course Objectives of Intro To TensorFlow:

  • Create a machine learning model in TensorFlow
  • Use TensorFlow libraries to solve numerical problems
  • Troubleshoot and debug common tensorflow code disadvantages
  • Use tf.estimator to create, train and evaluate ML models
  • Train, deploy, and produce ML models with the cloud ML engine

The skills that you can gain after completing this Intro to Tensorflow, a specialization of Machine Learning Courses by Google are:

  • Application Programming Interface (API),
  • Estimator,
  • Machine Learning,
  • Tensorflow,
  • Cloud Computing
PLATFORM: COURSERA
PROVIDER:GOOGLE CLOUD
COST:FREE (AUDIT)
DIFFICULTY:INTERMEDIATE
LANGUAGE:ENGLISH
CERTIFICATE:AVAILABLE (PAID)
EFFORT:8 HOURS
START DATE:ON GOING
DURATION:2 WEEKS
HURRY UP TO ENROLL FOR FREE.

Syllabus – Intro To TensorFlow

Week 1: Introduction

In this course, you will get introduced to the tool TensorFlow, which is used to write Machine Learning programs in this course. In the first course, you learned how to formulate business problems in the form of machine learning problems, and in the second course, you learned how the machine works in practice and how to create datasets that you can use to learn machine learning. Can. Now that you have the data, you are ready to start the machine learning program.

  • Introduction – (Video) 2 min
  • Intro to Qwiklabs – (Video) 5 min

Core TensorFlow

We will introduce you to the key components of TensorFlow and you will get hands-on practice building machine learning programs. You will compare lazy evaluation and mandatory programs and work with graphs, sessions, variables, as you eventually debug the TensorFlow program.

  • Introduction – (Video) 1 min
  • What is TensorFlow – (Video) 2 min
  • Benefits of a Directed Graph – (Video) 5 min
  • TensorFlow API Hierarchy – (Video) 3 min
  • Lazy Evaluation – (Video) 4 min
  • Graph and Session – (Video) 4 min
  • Evaluating a Tensor – (Video) 2 min
  • Visualizing a graph – (Video) 2 min
  • Tensors – (Video) 6 min
  • Variables – (Video) 6 min
  • Lab Intro: Writing low-level TensorFlow programs – (Video) 16 sec
  • Lab Solution – (Video) 8 min
  • Introduction – (Video) 5 min
  • Shape problems – (Video) 3 min
  • Fixing shape problems – (Video) 2 min
  • Data type problems – (Video) 1 min
  • Debugging full programs – (Video) 4 min
  • Intro: Debugging full programs – (Video) 15 sec
  • Demo: Debugging Full Programs – (Video) 3 min
  • What is TensorFlow? – (Practice Exercise) 30 min
  • Graphs and Sessions – (Practice Exercise) 30 min
  • Core TensorFlow – (Practice Exercise) 30 min

Week 2: Estimator API

In this module you will be able to understand the Estimator API.

  • Introduction – (Video) 1 min
  • Estimator API – (Video) 3 min
  • Pre-made Estimators – (Video) 5 min
  • Demo: Housing Price Model – (Video) 1 min
  • Checkpointing – (Video) 1 min
  • Training on in-memory datasets – (Video) 2 min
  • Lab Intro: Estimator API- (Video) 39 sec
  • Lab Solution: Estimator API – (Video) 10 min
  • Train on large datasets with Dataset API – (Video) 8 min
  • Lab Intro: Scaling up TensorFlow ingest using batching – (Video) 35 sec
  • Lab Solution: Scaling up TensorFlow ingest using batching – (Video) 5 min
  • Big jobs, Distributed training – (Video) 6 min
  • Monitoring with TensorBoard – (Video) 3 min
  • Demo: TensorBoard UI – (Video) 28 sec
  • Serving Input Function – (Video) 5 min
  • Recap: Estimator API – (Video) 1 min
  • Lab Intro: Creating a distributed training TensorFlow model with Estimator API – (Video) 51 sec
  • Lab Solution: Creating a distributed training TensorFlow model with Estimator API – (Video) 7 min
  • Estimator API – (Practice Exercise) 30 min

Week 3: Scaling TensorFlow models

  • Introduction – (Video) 31 sec
  • Why Cloud AI Platform? – (Video) 6 min
  • Train a Model – (Video) 2 min
  • Monitoring and Deploying Training Jobs – (Video) 2 min
  • Lab Intro: Scaling TensorFlow with Cloud AI Platform – (Video) 50 sec
  • Lab Solution: Scaling TensorFlow with Cloud AI Platform – (Video) 16 min
  • Cloud ML Engine is now Cloud AI Platform – (Reading) 10 min
  • Cloud AI Platform – (Video) 30 min

Summary

Here all the topics that have been covered so far in the TensorFlow topics have been covered. Google will re-release the core TensorFlow code, the Estimator API, and end with scaling our machine learning model with the cloud machine learning engine.

  • Summary – (Video) 2 min

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