TensorFlow for Computer Vision - Full Tutorial for Beginners

Overview

Dive into a comprehensive tutorial on using TensorFlow 2 and Python for computer vision projects. Learn to create image classification models, starting with a prepared dataset and progressing to a real-world problem that requires data cleaning and preparation. Master essential skills such as exploring datasets, building neural networks using sequential, functional, and Model Class approaches, and implementing data generators. Gain hands-on experience with the MNIST dataset, compile and fit models, add callbacks, evaluate performance, and make predictions on single images. Acquire practical knowledge on setting up your development environment, including Visual Studio Code and Miniconda, and understand the installation process for both CPU and GPU versions of TensorFlow 2. By the end of this tutorial, you'll have the skills to tackle computer vision projects and optimize your models for better performance.

Syllabus

Introduction.
Course outline.
Who’s this course for.
Why learn TensorFlow.
We will be using an IDE and not notebooks.
Visual Studio Code (how to download and install it).
Miniconda - how to install it.
Miniconda - why we need it.
How are we going to use conda virtual environments in VS Code?.
Installing Tensorflow 2 (CPU version).
Installing Tensorflow 2 (GPU version).
What do we want to achieve?.
Exploring MNIST dataset.
Tensorflow layers.
Building a neural network the sequential way.
Compiling the model and fitting the data.
Building a neural network the functional way.
Building a neural network the Model Class way.
Things we should add.
Restructuring our code for better readability.
First part summary.
What we want to achieve.
Downloading and exploring the dataset.
Preparing train and validation sets.
Preparing the test set.
Building a neural network the functional way.
Creating data generators.
Instantiating the generators.
Compiling the model and fitting the data.
Adding callbacks.
Evaluating the model.
Potential improvements.
Running prediction on single images.
Second part summary.
Where you can find me if you have questions.