Deep Learning: Convolutional Neural Networks for developers

本课程将教您以卷积神经网络架构为重点的深度学习。它的结构旨在帮助您从基础知识到高级概念真正学习深度学习。我们将开始学习 TensorFlow 和 Pytorch 等深度学习框架的底层原理，然后使用 Pytorch 转向高级深度学习架构。

在我们的旅程中，我们还将有一些项目探索深度学习和计算机视觉的一些关键概念，例如：什么是图像；什么是卷积；如何实现一个普通的神经网络；反向传播如何工作；如何使用迁移学习等等。

所有示例都是用 Python 和 Jupyter 笔记本编写的，并带有大量注释，以帮助您遵循实现。即使您不太了解 Python，您也可以按照代码并从示例中学习。

该项目的高级部分将需要 GPU，但不用担心，因为这些示例只需一键即可在 Google Colab 上运行，无需设置，而且是免费的！您只需要拥有一个 Google 帐户。

This course will teach you Deep learning focusing on Convolution Neural Networks architectures

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

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.