Coursera

Data Pipelines with TensorFlow Data Services (Coursera)

Offered by DeepLearning.AI,

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In this third course, you will:

Perform streamlined ETL tasks using TensorFlow Data Services
Load different datasets and custom feature vectors using TensorFlow Hub and TensorFlow Data Services APIs
Create and use pre-built pipelines for generating highly reproducible I/O pipelines for any dataset
Optimize data pipelines that become a bottleneck in the training process
Publish your own datasets to the TensorFlow Hub library and share standardized data with researchers and developers around the world

This Specialization builds upon our TensorFlow in Practice Specialization. If you are new to TensorFlow, we recommend that you take the TensorFlow in Practice Specialization first. To develop a deeper, foundational understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

What You Will Learn

Perform efficient ETL tasks using Tensorflow Data Services APIs
Construct train/validation/test splits of any dataset - either custom or present in TensorFlow Hub Dataset library - using Splits API
Use different modules and functions of the TFDS API to prepare your data for training pipelines
Identify bottlenecks in your input pipelines and increase your workflow efficiency by input parallelization

We recommend taking Course 1 of the TensorFlow in Practice Specialization first, or have a basic familiarity with building models in TensorFlow

Course 3 of 4 in the TensorFlow: Data and Deployment Specialization.

Syllabus

WEEK 1
Data Pipelines with TensorFlow Data Services
This week, you will be able to perform efficient ETL tasks using Tensorflow Data Services APIs

WEEK 2
Splits and Slices API for Datasets in TF
In this week, you will construct train/validation/test splits of any dataset - either custom or present in TensorFlow hub dataset library - using Splits API

WEEK 3
Exporting Your Data into the Training Pipeline
This week you will extend your knowledge of data pipelines

WEEK 4
Performance
You'll learn how to handle your data input to avoid bottlenecks, race conditions and more!

Go to Class

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