Introduction to TensorFlow

Focused View

Google Cloud

2:59:42

14 View

1 - Introduction

01 - Introduction.mp4

02:13

02 - Getting Started with Google Cloud Platform and Qwiklabs.mp4

03:43

2 - Core TensorFlow

03 - Introduction.mp4

01:37

04 - What is TensorFlow.mp4

02:38

05 - Benefits of a DAG.mp4

05:55

06 - TensorFlow API Hierarchy.mp4

03:59

07 - Lazy Evaluation.mp4

04:48

08 - Graph and Session.mp4

04:23

09 - Evaluating a Tensor.mp4

02:14

10 - Visualizing a graph.mp4

02:46

11 - Tensors.mp4

06:15

12 - Variables.mp4

06:53

13 - Lab Intro -Writing low-level TensorFlow programs.mp4

00:16

14 - [ML on GCP C3] Writing low-level TensorFlow programs.mp4

00:10

15 - Lab Solution.mp4

08:12

16 - Introduction to Debugging TensorFlow Programs.mp4

05:42

17 - Challenge - Shape problems.mp4

03:31

18 - Fixing shape problems.mp4

02:53

19 - Data type problems.mp4

01:59

20 - Debugging full programs.mp4

04:19

21 - Demo Intro -Debugging full programs.mp4

00:15

22 - Demo -Debugging full programs.mp4

03:57

3 - Estimator API

23 - Introduction.mp4

01:13

24 - Estimator API.mp4

03:20

25 - Pre-made Estimators.mp4

05:00

26 - Demo -Housing Price Model.mp4

01:58

27 - Checkpointing.mp4

01:36

28 - Training on in-memory datasets.mp4

02:42

29 - Lab Intro -Estimator API.mp4

00:39

30 - [ML on GCP C3] Implementing a Machine Learning model in TensorFlow using Estimator API.mp4

00:10

31 - Lab Solution -Estimator API.mp4

10:33

32 - Train on large datasets with Dataset API.mp4

08:28

33 - Lab Intro -Scaling up TensorFlow ingest using batching.mp4

00:35

34 - [ML on GCP C3] Scaling up TensorFlow ingest using batching.mp4

00:10

35 - Lab Solution -Scaling up TensorFlow ingest using batching.mp4

05:28

36 - Big jobs, Distributed training.mp4

06:16

37 - Monitoring with TensorBoard.mp4

03:40

38 - Demo -TensorBoard UI.mp4

00:28

39 - Serving Input Function L15a.mp4

05:49

40 - Serving Input Function L15b.mp4

01:07

41 - Lab Intro -Creating a distributed training TensorFlow model with Estimator API.mp4

00:51

42 - [ML on GCP C3] Creating a distributed training TensorFlow model with Estimator API.mp4

00:10

43 - Lab Solution -Creating a distributed training TensorFlow model with Estimator API.mp4

07:20

44 - Summary.mp4

01:09

4 - Scaling TensorFlow models with Cloud AI Platform

45 - Introduction.mp4

00:43

46 - Why Cloud AI Platform.mp4

06:47

47 - Train a Model.mp4

02:33

48 - Monitoring Training Jobs.mp4

02:21

49 - Lab Intro -Scaling TensorFlow with Cloud AI Platform.mp4

00:50

50 - [ML on GCP C3] Scaling TensorFlow with Cloud MLE.mp4

00:10

51 - Lab Solution -Scaling TensorFlow with Cloud AI Platform.mp4

16:26

5 - Summary

52 - Summary.mp4

02:32

Description

What You'll Learn?

In this course, you will learn how to create machine learning models in TensorFlow which is the tool we will use to write machine learning programs. You’ll learn how to use the TensorFlow libraries to solve numerical problems. When writing programs, you often want to know about common mistakes that you might run into, and how to fix common errors. Then, we’ll look at the Estimator API, which provides the highest level abstraction within TensorFlow for training, evaluating and serving machine learning models. You will learn how to Use tf_estimator to create, train, and evaluate an ML model. Finally, you’ll learn how to execute TensorFlow models on Cloud AI Platform, Google-managed infrastructure to run TensorFlow. You will learn how to Train, deploy, and productionalize ML models at scale with Cloud AI Platform.

More details

User Reviews

Rating

average 0

Total votes0

Focused display

TensorFlow

Google Cloud

Instructor's Courses

Google Cloud can help solve your toughest problems and grow your business. With Google Cloud, their infrastructure is your infrastructure. Their tools are your tools. And their innovations are your innovations.

Pluralsight

View courses Pluralsight

Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.