Deep Learning: Advanced Computer Vision (GANs, SSD, +More!) Course
VGG, ResNet, Inception, SSD, RetinaNet, Neural Style Transfer, GANs +More in Tensorflow, Keras, and Python
Latest update: Instead of SSD, I show you how to use RetinaNet, which is better and more modern. I show you both how to use a pretrained model and how to train one yourself with a custom dataset on Google Colab.
This is one of the most exciting courses I’ve done, and it really shows how fast and how far deep learning has come over the years. When I first started my deep learning series, I didn’t ever consider that I’d make two courses on convolutional neural networks.
In this course, you’ll see how we can turn a CNN into an object detection system, that not only classifies images but can locate each object in an image and predict its label. You can imagine that such a task is a basic prerequisite for self-driving vehicles. (It must be able to detect cars, pedestrians, bicycles, traffic lights, etc. in real-time). We’ll be looking at a state-of-the-art algorithm called SSD which is both faster and more accurate than its predecessors. Another very popular computer vision task that makes use of CNNs is called neural style transfer.
Best Seller Course: Deep Learning: Convolutional Neural Networks in Python
What you’ll learn
- Understand and apply transfer learning.
- Understand and use state-of-the-art convolutional neural nets such as VGG, ResNet and Inception
- Understand and use object detection algorithms like SSD
- Understand and apply neural style transfer
- Understand state-of-the-art computer vision topics
- Class Activation Maps
- GANs (Generative Adversarial Networks)
- Object Localization Implementation Project
Recommended Course: Deep Learning and Computer Vision A-Z™: OpenCV, SSD & GANs
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