DeepLearning.AI - Data Pipelines with TensorFlow Data Services
- Offered byCoursera
Data Pipelines with TensorFlow Data Services at Coursera Overview
Duration | 11 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Data Pipelines with TensorFlow Data Services at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 3 of 4 in the TensorFlow: Data and Deployment Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level We recommend taking Course 1 of the TensorFlow in Practice Specialization first, or have a basic familiarity with building models in TensorFlow
- Approx. 11 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Data Pipelines with TensorFlow Data Services at Coursera Course details
- Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model.
- 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.
Data Pipelines with TensorFlow Data Services at Coursera Curriculum
Data Pipelines with TensorFlow Data Services
A conversation with Andrew Ng
Introduction
Popular Datasets
Data Pipelines
Extract, Transform and Load
Versioning Datasets
Looking at the Notebook
Using TFDS in Keras to Train Fashion MNIST
Horses or Humans in TFDS
Week 1 Wrap Up
Downloading the Coding Examples and Exercises
Try Out the Notebook Yourself
Try the Horses or Human Notebook
Grader Note
Week 1 Quiz
Splits and Slices API for Datasets in TF
Introduction
Introduction to Splits API
Splits API Notebook Walkthrough
File Structure in TensorFlow Datasets
Feature Descriptors
TFRecord Colab Walkthrough
Week 2 Wrap Up
Splits API Colab
TFRecord Colab
Grader Note
Week 2
Exporting Your Data into the Training Pipeline
A Conversation with Andrew Ng
Introduction
Input Data
Basic Mechanics
Numeric and Bucketized Columns
Vocabulary and Hashed Columns, Feature Crossing
Embedding Columns
Introduction
Notebook Walkthrough
Introduction
Numpy, Pandas and Images
CSV
Text and TFRecord
Generators
Introduction
Notebook walkthrough
Introduction
Using Numpy and Pandas
Image Data
CSV Data
Text Data
Link to the Notebook
Link to the CNN Course
Link to the Notebook
CSV Colab
Link to the Course
Week 3 Quiz
Performance
A conversation with Andrew Ng
Introduction
ETL
What Happens When You Train a Model
Introduction
Caching
Parallelism APIs
Autotuning
Parallelizing Data Extraction
Best Practices for Code Improvements
A Few Words by Laurence
A conversation with Andrew Ng
Introduction
How to Start Using a Dataset
Implementation
File Access and Possible Problems in Data
Publishing the Dataset
Introduction
Going Through the Colab- Part 1
Going Through the Colab - Part 2
Closing Words
A conversation with Andrew Ng
URLs
Link to the Colab
Week 4 Quiz
Publishing your Dataset Quiz
Data Pipelines with TensorFlow Data Services at Coursera Admission Process
Important Dates
Other courses offered by Coursera
Student Forum
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