This course on TensorFlow for computer vision aims to teach learners how to use TensorFlow 2 and Python to create computer vision projects. By the end of the course, students will be able to build image classification models, work with datasets, clean and prepare data, and evaluate model performance. The course covers topics such as installing TensorFlow, exploring datasets, building neural networks in different ways, creating data generators, and running predictions on images. The teaching method involves practical demonstrations and hands-on coding projects. This course is designed for beginners interested in computer vision, machine learning, and TensorFlow.
Overview
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.
Taught by
freeCodeCamp.org
