What you'll learn:
- Convolutional neural network architectures
- Computer vision algorithims
- How to implement a Deep Neural Network from scratch
- How back-propagation algorithm works
- How to search similar images
- How to build multi task models
This course will teach you Deep learning focusing on Convolution Neural Net architectures. It is structured to help you genuinely learn Deep Learning by starting from the basics until advanced concepts. We will begin learning what it is under the hood of Deep learning frameworks like Tensorflow and Pytorch, then move to advanced Deep learning Architecture with Pytorch.
During our journey, we will also have projects exploring some critical concepts of Deep learning and computer vision, such as: what is an image; what are convolutions; how to implement a vanilla neural network; how back-propagation works; how to use transfer learning and more.
All examples are written in Python and Jupyter notebooks with tons of comments to help you to follow the implementation. Even if you don’t know Python well, you will be able to follow the code and learn from the examples.
The advanced part of this project will require GPU but don’t worry because those examples are ready to run on Google Colab with just one click, no setup required, and it is free! You will only need to have a Google account.
By following this course until the end, you will get insights, and you will feel empowered to leverage all recent innovations in the Deep Learning field to improve the experience of your projects.
