Computer Vision with Python (2022)

在本课程中，您将学习如何使用 Python 编程语言构建用于图像分类和对象检测的计算机视觉算法。我们将首先了解神经网络基础知识：什么是神经网络，神经网络背后的理论是什么，然后我们将讨论用于分类 MNIST 数据集的二进制分类器，例如 SVM，学生将学习如何对手写数字进行分类MNIST 数据集分为多个类。我们将讨论用于检测图像边缘的不同类型的边缘检测器。在此之后，我们将讨论卷积神经网络：它们是如何构建的，最常见和最有效的 CNN 架构是什么，以及如何在 Python 中实现它们。对象检测和穷举搜索的主题也将被处理。课程的最后一部分将是一个示例应用程序，用于在云上构建和训练自定义构建的卷积神经网络，以对来自开源数据集的图像进行分类。从获取数据、读取数据、构建网络和在云上训练网络的所有步骤都将被仔细解释，以便学生有一个工作示例，可以为自己的目的重用或修改

Published 04/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 20 lectures (1h 2m) | Size: 515.1 MB

Mastering Computer Vision with the Python programming Language

What you’ll learn
Computer Vision
Neural Networks
Object detection
Build and Train your own Computer Vision Model

Requirements
Basic Python
Basic Linux Commands

Description
In this class you will learn how to build Computer Vision algorithms for Image classification and Object detection using the Python Programming Language. We will first go through Neural Networks Basics: what are Neural networks , what is the theory behind neural networks , then we will talk about binary classifiers like an SVM for classifying the MNIST datasets, Students will learn how to classify the hand written digits of the MNIST dataset into multiple classes. We will discuss the different types of edge detectors to detect edges in images. After this we will discuss convolutionnal neural networks: how are they built, what are the most common and efficient CNN architectures and how do you implement them in Python. The topic of Object detection and Exhaustive search will also be dealt with. The last part of the class will be an example application of building and training a custom built Convolutionnal neural Network on the cloud to classify images from an open source dataset. All the steps from getting data, reading the data , building the network and training the network on the cloud will be carefully explained so that the student has a working example to be able to reuse or modify for its own purpose

Who this course is for
Computer Science Students
Computer Vision scientists