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What do we want to achieve?
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Classroom Contents
TensorFlow for Computer Vision - Full Tutorial for Beginners
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- 1 Introduction
- 2 Course outline
- 3 Who’s this course for
- 4 Why learn TensorFlow
- 5 We will be using an IDE and not notebooks
- 6 Visual Studio Code (how to download and install it)
- 7 Miniconda - how to install it
- 8 Miniconda - why we need it
- 9 How are we going to use conda virtual environments in VS Code?
- 10 Installing Tensorflow 2 (CPU version)
- 11 Installing Tensorflow 2 (GPU version)
- 12 What do we want to achieve?
- 13 Exploring MNIST dataset
- 14 Tensorflow layers
- 15 Building a neural network the sequential way
- 16 Compiling the model and fitting the data
- 17 Building a neural network the functional way
- 18 Building a neural network the Model Class way
- 19 Things we should add
- 20 Restructuring our code for better readability
- 21 First part summary
- 22 What we want to achieve
- 23 Downloading and exploring the dataset
- 24 Preparing train and validation sets
- 25 Preparing the test set
- 26 Building a neural network the functional way
- 27 Creating data generators
- 28 Instantiating the generators
- 29 Compiling the model and fitting the data
- 30 Adding callbacks
- 31 Evaluating the model
- 32 Potential improvements
- 33 Running prediction on single images
- 34 Second part summary
- 35 Where you can find me if you have questions
