Image Understanding with TensorFlow on GCP

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Google Cloud

4:19:50

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00. Welcome to Image Understanding with TensorFlow on GCP

00. Course Introduction.mp4

03:09

01. Images as Visual Data.mp4

05:19

02. Structured vs Unstructured Data.mp4

06:24

03. Getting started with GCP and Qwiklabs.mp4

03:48

01. Linear and DNN Models

00. Introduction.mp4

07:00

01. Linear Models.mp4

06:35

02. Lab Intro - Linear Models for Image Classification.mp4

00:46

03. Image Classification with a Linear Model.mp4

00:10

04. Lab Solution - Linear Models for Image Classification.mp4

11:30

05. DNN Models Review.mp4

03:41

06. Lab Intro - DNN Models for Image Classification.mp4

00:47

07. Image Classification with a Deep Neural Network Model.mp4

00:10

08. Lab Solution - DNN Models for Image Classification.mp4

19:57

09. Review - What is Dropout - .mp4

03:06

10. Lab Intro - DNNs with Dropout Layer for Image Classification.mp4

00:23

11. Image Classification with a DNN Model with Dropout.mp4

00:10

12. Lab Solution - DNNs with Dropout Layer for Image Classification.mp4

11:38

02. Convolutional Neural Networks (CNNs)

00. Introduction.mp4

05:37

01. Understanding Convolutions.mp4

08:20

02. CNN Model Parameters.mp4

04:59

03. Working with Pooling Layers.mp4

02:33

04. Implementing CNNs with TensorFlow.mp4

04:02

05. Lab Intro - Creating an Image Classifier with a Convolutional Neural Network.mp4

02:13

06. Image Classification with a CNN Model.mp4

00:10

07. Lab Solution - Creating an Image Classifier with a Convolutional Neural Network.mp4

10:02

03. Dealing with Data Scarcity

00. The Data Scarcity Problem.mp4

05:39

01. Data Augmentation.mp4

08:50

02. Lab Intro - Implementing image augmentation.mp4

01:05

03. Image Augmentation in TensorFlow.mp4

00:10

04. Lab Solution - Implementing image augmentation.mp4

02:56

05. Transfer Learning.mp4

05:11

06. Lab Intro - Implementing Transfer Learning.mp4

00:47

07. Image Classification Transfer Learning with Inception v3.mp4

00:10

08. Lab Solution - Implementing Transfer Learning.mp4

08:18

09. No Data, No Problem.mp4

01:53

04. Going Deeper Faster

00. Introduction.mp4

08:34

01. Batch Normalization.mp4

07:40

02. Residual Networks.mp4

06:52

03. Accelerators (CPU vs GPU, TPU).mp4

05:08

04. TPU Estimator.mp4

09:51

05. Demo - TPU Estimator.mp4

18:06

06. Neural Architecture Search.mp4

03:40

07. Summary.mp4

04:51

05. Pre-built ML Models for Image Classification

00. Introduction.mp4

01:26

01. Pre-built ML Models.mp4

05:31

02. Cloud Vision API.mp4

02:15

03. Demo - Vision API.mp4

01:03

04. AutoML Vision.mp4

01:27

05. Demo - AutoML.mp4

04:32

06. AutoML Architecture.mp4

02:12

07. Lab Intro - Training with Pre-built ML Models using Cloud Vision API and AutoML.mp4

00:39

08. Training with Pre-built ML Models using Cloud Vision API and AutoML.mp4

00:10

09. Lab Solution - Training with Pre-built ML Models using Cloud Vision API and AutoML.mp4

14:27

06. Summary

00. Summary.mp4

03:58

Description

In this course, we will take a look at different strategies for building an image classifier using convolutional neural networks. We'll improve the model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting our data. We will also look at practical issues that arise, for example, when you don’t have enough data and how to incorporate the latest research findings into our models. You will get hands-on practice building and optimizing your own image classification models on a variety of public datasets in the labs we’ll work on together.

What You'll Learn?

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Machine Learning

Artificial Intelligence

TensorFlow

Google Cloud

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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

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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.