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.
Learning Journey Context
Designed for experienced practitioners. We recommend having a solid grasp of Information Technology fundamentals before starting this specialization.
Relevant for professionals pursuing roles within Information Technology.
Quick Facts
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.
Outcomes
- Welcome to Image Understanding with TensorFlow on GCP : 18mins.
- Linear and DNN Models : 65mins.
- Convolutional Neural Networks (CNNs) : 37mins.
- Dealing with Data Scarcity : 35mins.
- Going Deeper Faster : 64mins.
- Pre-built ML Models for Image Classification : 33mins.
- Summary : 3mins.
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