TensorFlow for Computer Vision Course - Full Python Tutorial for Beginners

Learn how to use TensorFlow 2 and Python for computer vision in this complete course. The course shows you how to create two computer vision projects. The first involves an image classification model with a prepared dataset. The second is a more real-world problem where you will have to clean and prepare a dataset before using it. 💻 Code: https://github.com/sniper0110/IntroductionToTensorflow2 ✏️ Nour Islam Mokhtari created this course. Connect with him here: https://withkoji.com/@Nour_Islam 🔗 Get Nour's free Machine Learning job-ready checklist: https://www.aifee.co/free-resources ❤️ Try interactive Python courses we love, right in your browser: https://scrimba.com/freeCodeCamp-Python (Made possible by a grant from our friends at Scrimba) ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Introduction ⌨️ (0:01:21) Course outline ⌨️ (0:05:11) Who’s this course for ⌨️ (0:05:35) Why learn TensorFlow ⌨️ (0:06:25) We will be using an IDE and not notebooks ⌨️ (0:07:25) Visual Studio Code (how to download and install it) ⌨️ (0:10:50) Miniconda - how to install it ⌨️ (0:13:23) Miniconda - why we need it ⌨️ (0:17:24) How are we going to use conda virtual environments in VS Code? ⌨️ (0:21:20) Installing Tensorflow 2 (CPU version) ⌨️ (0:29:56) Installing Tensorflow 2 (GPU version) ⌨️ (0:43:34) What do we want to achieve? ⌨️ (0:45:26) Exploring MNIST dataset ⌨️ (1:05:54) Tensorflow layers ⌨️ (1:09:44) Building a neural network the sequential way ⌨️ (1:27:22) Compiling the model and fitting the data ⌨️ (2:00:52) Building a neural network the functional way ⌨️ (2:08:33) Building a neural network the Model Class way ⌨️ (2:14:31) Things we should add ⌨️ (2:18:29) Restructuring our code for better readability ⌨️ (2:23:11) First part summary ⌨️ (2:24:12) What we want to achieve ⌨️ (2:25:23) Downloading and exploring the dataset ⌨️ (2:34:20) Preparing train and validation sets ⌨️ (2:53:37) Preparing the test set ⌨️ (3:10:17) Building a neural network the functional way ⌨️ (3:22:12) Creating da