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Course
- AI
- Cloud
Image Understanding with TensorFlow on GCP
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
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.
Table of contents
- Introduction | 7m
- Linear Models | 6m 34s
- Lab Intro:Linear Models for Image Classification | 46s
- Lab: Image Classification with a Linear Model | 10s
- Lab Solution:Linear Models for Image Classification | 11m 30s
- DNN Models Review | 3m 41s
- Lab Intro:DNN Models for Image Classification | 47s
- Lab: Image Classification with a Deep Neural Network Model | 10s
- Lab Solution:DNN Models for Image Classification | 19m 57s
- Review:What is Dropout? | 3m 6s
- Lab Intro:DNNs with Dropout Layer for Image Classification | 23s
- Lab: Image Classification with a DNN Model with Dropout | 10s
- Lab Solution:DNNs with Dropout Layer for Image Classification | 11m 38s
