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TensorFlow for Computer Vision Course - Full Python Tutorial for Beginners

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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

???? 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

⭐️ 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 data generators

⌨️ (3:31:39) Instantiating the generators

⌨️ (3:35:37) Compiling the model and fitting the data

⌨️ (3:40:34) Adding callbacks

⌨️ (3:52:08) Evaluating the model

⌨️ (3:58:04) Potential improvements

⌨️ (4:08:49) Running prediction on single images

⌨️ (4:23:05) Second part summary

⌨️ (4:23:56) Where you can find me if you have questions

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