TensorFlow for AI: Applying Image Convolution

This guided project course is part of the "Tensorflow for AI" series, and this series presents material that builds on the first course of DeepLearning.AI TensorFlow Developer Professional Certificate, which will help learners reinforce their skills and build more projects with Tensorflow. In this 1.5-hour long project-based course, you will discover convolutions, apply filters to images, apply pooling layers, and try out the convolution and pooling techniques on real images to learn about how convolutions work. At the end of the project, you will get a bonus deep learning project implemented with Tensorflow. By the end of this project, you will have learned how convolutions work and how to create convolutional layers to prepare for your own deep learning projects using convolutional neural networks. This class is for learners who want to 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 knowledge-based course about convolutions in images with TensorFlow. Also, this project provides learners with needed knowledge about building convolutional neural networks and improves their skills in applying filters to images which helps them in fulfilling their career goals by adding this project to their portfolios.

• Tensorflow for AI: Applying Image Convolution
• Welcome to this project-based course on Applying Image Convolution. This guided project course is part of the "Tensorflow for AI" series, and this series presents material that builds on the first course of DeepLearning.AI TensorFlow Developer Professional Certificate offered at Coursera, which will help learners reinforce their skills and build more projects with Tensorflow.In this 1-hour long project-based course, you will learn how to create convolutions and pooling layers with filters and build convolutional layers for convolutional neural networks with Tensorflow, and you will get a bonus deep learning project implemented with Tensorflow. By the end of this project, you will have applied convolutions with filters and pooling layers for neural networks.