Instructors: Alexsandro Souza
1 section • 12 lectures • 2h 49m total length
Video: MP4 1280×720 44 KHz | English + Sub
Updated 10/2022 | Size: 1.3 GB
This course will teach you Deep learning focusing on Convolution Neural Networks architectures
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
Requirements
No Deep Learning experience needed. You will learn everything you need to know
Description
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.
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