TensorFlow for CNNs: Learn and Practice CNNs

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Coursera Project Network

Learn the fundamentals of Convolutional Neural Networks

Learn how to build deep learning image classification models

Learn how to create a convolutional neural network from scratch with Tensorflow

Clock2 hours
Comment Dots英语(English)

This guided project course is part of the "Tensorflow for Convolutional Neural Networks" series, and this series presents material that builds on the second course of DeepLearning.AI TensorFlow Developer Professional Certificate, which will help learners reinforce their skills and build more projects with Tensorflow. In this 2-hour long project-based course, you will learn the fundamentals of CNNs, structure, components, and how they work, and you will learn practically how to solve an image classification deep learning task in the real world and create, train, and test a neural network with Tensorflow using real-world images, and you will get a bonus deep learning exercise implemented with Tensorflow. By the end of this project, you will have learned the fundamentals of convolutional neural networks and created a deep learning model with TensorFlow on a real-world dataset. This class is for learners who want to learn how to work with convolutional neural networks and use Python for building convolutional neural networks with TensorFlow, and for learners who are currently taking a basic deep learning course or have already finished a deep learning course and are searching for a practical deep learning project with TensorFlow. Also, this project provides learners with further knowledge about creating and training convolutional neural networks and improves their skills in Tensorflow which helps them in fulfilling their career goals by adding this project to their portfolios.


  • Deep Learning
  • Artificial Neural Network
  • Convolutional Neural Network
  • Python Programming
  • Tensorflow



  1. Introduction and overview of the project

  2. Understand the Structure of Neural Networks

  3. Components of Convolutional Neural Networks

  4. Import the Dataset and Preprocess the Data

  5. Create and Train the Model

  6. Test the Model and Make Predictions






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