This course was created with the
course builder. Create your online course today.
Start now
Create your course
with
Autoplay
Autocomplete
Previous Lesson
Complete and Continue
Introduction to Tensorflow 2 for Computer Vision
Introduction and outline
Course outline (3:50)
Who’s this course for (0:24)
Why learn Tensorflow (0:49)
Software setup
We will be using an IDE and not notebooks (0:59)
Visual Studio Code (how to download and install it) (3:24)
Miniconda - how to install it (2:33)
Miniconda - why we need it (4:00)
How are we going to use conda virtual environments in VS Code? (3:56)
Installing Tensorflow 2 (CPU version) (8:36)
Installing Tensorflow 2 (GPU version) (13:38)
Image classification : MNIST example
What do we want to achieve? (1:51)
Exploring MNIST dataset (20:28)
Tensorflow layers (3:49)
Building a neural network the sequential way (17:38)
Compiling the model and fitting the data (33:29)
Building a neural network the functional way (7:41)
Building a neural network the Model Class way (5:57)
Things we should add (3:57)
Restructuring our code for better readability (4:42)
Summary (1:00)
Image classification : German Traffic Signs
What we want to achieve (1:11)
Downloading and exploring the dataset (8:57)
Preparing train and validation sets (19:16)
Preparing the test set (16:40)
Building a neural network the functional way (11:55)
Creating data generators (9:26)
Instantiating the generators (3:58)
Compiling the model and fitting the data (4:57)
Adding callbacks (11:33)
Evaluating the model (5:56)
Potential improvements (10:45)
Running prediction on single images (14:15)
Summary (0:51)
Where to go from here?
Your next steps (9:56)
Compiling the model and fitting the data
Lesson content locked
If you're already enrolled,
you'll need to login
.
Enroll in Course to Unlock