Sequence Models for Time Series and Natural Language Processing on Google Cloud

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Google Cloud

4:31:22

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01 - Working with Sequences

01 - Course Introduction.mp4

01:53

02 - Sequence data and models.mp4

05:24

03 - From sequences to inputs.mp4

02:43

04 - Modeling sequences with linear models.mp4

02:53

05 - Getting started with GCP and Qwiklabs.mp4

03:48

06 - Lab intro -using linear models for sequences.mp4

00:20

07 - Time Series Prediction with a Linear Model.mp4

00:10

08 - Lab solution -using linear models for sequences.mp4

07:12

09 - Modeling sequences with DNNs.mp4

02:44

10 - Lab intro -using DNNs for sequences.mp4

00:19

11 - Time Series Prediction with a DNN Model.mp4

00:10

12 - Lab solution -using DNNs for sequences.mp4

02:18

13 - Modeling sequences with CNNs.mp4

03:35

14 - Lab intro -using CNNs for sequences.mp4

00:19

15 - Time Series Prediction with a CNN Model.mp4

00:10

16 - Lab solution -using CNNs for sequences.mp4

03:45

17 - The variable-length problem.mp4

04:24

02 - Recurrent Neural Networks

18 - Introducing Recurrent Neural Networks.mp4

03:45

19 - How RNNs represent the past.mp4

04:21

20 - The limits of what RNNs can represent.mp4

05:04

21 - The vanishing gradient problem.mp4

01:52

03 - Dealing with Longer Sequences

22 - Introduction.mp4

03:02

23 - LSTMs and GRUs.mp4

06:19

24 - RNNs in TensorFlow.mp4

02:09

25 - Lab Intro - Time series prediction -end-to-end (rnn).mp4

00:45

26 - Time Series Prediction with a RNN Model.mp4

00:10

27 - Lab Solution - Time series prediction -end-to-end (rnn).mp4

10:04

28 - Deep RNNs.mp4

01:29

29 - Lab Intro - Time series prediction -end-to-end (rnn2).mp4

00:26

30 - Time Series Prediction with a Two-Layer RNN Model.mp4

00:10

31 - Lab Solution - Time series prediction -end-to-end (rnn2).mp4

06:40

32 - Improving our Loss Function.mp4

02:44

33 - Demo - Time series prediction -end-to-end (rnnN).mp4

03:52

34 - Working with Real Data.mp4

10:47

35 - Lab Intro - Time Series Prediction - Temperature from Weather Data.mp4

01:01

36 - An RNN Model for Temperature Data.mp4

00:10

37 - Lab Solution - Time Series Prediction-Temperature from Weather Data.mp4

11:32

38 - Summary.mp4

01:08

04 - Text Classification

39 - Working with Text.mp4

01:27

40 - Text Classification.mp4

06:35

41 - Selecting a Model.mp4

02:33

42 - Lab Intro - Text Classification.mp4

00:47

43 - Text Classification using TensorFlow_Keras on AI Platform.mp4

00:10

44 - Lab Solution - Text Classification.mp4

11:29

45 - Python vs Native TensorFlow.mp4

04:03

46 - Demo -Text Classification with Native TensorFlow.mp4

07:06

47 - Summary.mp4

01:08

05 - Reusable Embeddings

48 - Historical methods of making word embeddings.mp4

06:02

49 - Modern methods of making word embeddings.mp4

08:44

50 - Introducing TensorFlow Hub.mp4

01:39

51 - Lab Intro - Evaluating a pre-trained embedding from TensorFlow Hub.mp4

00:24

52 - Using pre-trained embeddings with TensorFlow Hub.mp4

00:10

53 - Lab Solution - TensorFlow Hub.mp4

09:56

54 - Using TensorFlow Hub within an estimator.mp4

01:17

06 - Encoder-Decoder Models

55 - Introducing Encoder-Decoder Networks.mp4

09:33

56 - Attention Networks.mp4

04:31

57 - Training Encoder-Decoder Models with TensorFlow.mp4

06:27

58 - Introducing Tensor2Tensor.mp4

11:10

59 - Lab Intro - Cloud poetry -Training custom text models on Cloud ML Engine.mp4

01:12

60 - Text generation using tensor2tensor on Cloud AI Platform.mp4

00:10

61 - Lab Solution - Cloud poetry -Training custom text models on Cloud ML Engine.mp4

25:25

62 - AutoML Translation.mp4

04:57

63 - Dialogflow.mp4

06:54

64 - Lab Intro - Introducing Dialogflow.mp4

00:54

65 - Getting Started with Dialogflow.mp4

00:10

66 - Lab Solution - Dialogflow.mp4

13:01

07 - Summary

67 - Summary.mp4

03:51

Description

In this course, we’ll learn how to make predictions on sequences of data.

What You'll Learn?

In this course, we’ll learn how to make predictions on sequences of data. We’ll cover common business use cases like- 1.time-series prediction and how to deal with more recent data points getting more relevance 2.translating entire sentences (aka sequences of words) into other languages You will get hands-on practice building and optimizing your own text classification and sequence models on a variety of public datasets in the labs we’ll work on together.

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Natural Language Processing

Time Series Analysis

Google Cloud

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Pluralsight

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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.