Convolutional Neural Networks in TensorFlow

DeepLearning.AI extends an introductory TensorFlow workflow with larger datasets and techniques intended to improve generalization. Data augmentation leads into transfer learning and multiclass classification. The course is a practical continuation for learners who can already build a basic network.

What you will learn

• Prepare a larger image-classification workflow.
• Use augmentation and transfer learning in a CNN project.
• Evaluate predictions across several image classes.

Who this suits

Learners with the first TensorFlow specialization course, Python coding and high-school mathematics. Prior ML or deep-learning experience is helpful.

Before you enroll

Check the TensorFlow and Keras versions used by the notebooks. APIs and model formats can change; reproduce a small example before investing time in a larger project.

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Put it into practice

Compare a model with and without augmentation. Inspect class-level errors and explain whether the change improves generalization on untouched examples.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

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