Convolutional Neural Networks with Pytorch

在本课程中，您将学习什么是卷积神经网络模型以及如何实现它。为此，您将使用 Python 编程语言使用库 Pytorch，该工具可让您轻松构建神经网络模型。

在课程的第一部分，您将学习有关深度学习和卷积神经网络理论的基础知识，然后我们将深入研究这些模型背后的数学原理，以便您了解这些模型的工作原理、训练方式以及评估并利用这些知识，您可以更改内容以改进您自己的模型。

在理论部分之后，您将构建一个实际项目，在该项目中您将构建一个卷积神经网络模型以将图像分类为不同的类别（例如将狗的图片分类为狗）。为此，您将使用 Pytorch，这是一个允许您使用 Python 创建和训练神经网络模型的库。首先，您将使用特定的卷积架构创建模型，然后您将应用在理论部分学到的所有概念来训练模型。训练后，您将获得指标，这些指标将允许您分析和评估您的模型有多好。根据结果​​，您将学习如何在项目中实施更改，以便您可以学习如何探索不同的选项以提高模型性能。

您不需要先前的深度学习、卷积神经网络或 Pytorch 知识，因为您将学习使用该技术构建深度学习项目所需的基础知识。

因此，通过这门非常实用的课程开始构建卷积深度学习项目。

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 22 lectures (4h 45m) | Size: 4.1 GB
Learn how to implement a Convolutional Neural Network using Pytorch

What you’ll learn:
Learn what is a Convolutional Neural Network
Learn what is Deep Learning
Learn what is a Neural Network
Learn what is convolution
Learn parameters required in convolution
Learn different layers in a convolutional neural network
Learn the mathematics behind the neural networks
Learn how to train a neural network
Learn how to use Pytorch framework to build a Convolutional Neural Network model
Learn how to use Python to build a Deep Learning project
Learn how to use Jupyter notebook to build a Deep Learning project
Learn how to create and train a neural network model with Pytorch
Learn how to analyze a neural network training and measure the performance while training
Learn how to evaluate a neural network model and get metrics to analyze general performance
Learn how to apply changes and exploration analysis in order to improve the neural network model

Requirements
Basic Python knowledge is required
No previous knowledge in Convolutional Neural Networks is required
No previous knowledge in Deep Learning is required
No previous knowledge in Machine Learning is required
No previous knowledge in Pytorch is required

Description
In this course, you are going to learn what is and how to implement a Convolutional Neural Network model. To do this, you will use the library Pytorch using Python programming language, a tool that allows you to easily build a neural network model.

In the first part of the course, you will learn the basic about the theory of Deep Learning and Convolutional Neural Networks and then we will go deeper in the mathematics behind these models so that you can understand how these models works, how they are trained and evaluated and with this knowledge, you can change stuff in order to improve your own models.

After the theoretical part, you will build a practical project in which you will build a convolutional neural network model to classify images into different classes (like classify a picture of a dog as a dog). To do this you will use Pytorch, a library that allows you to create and train a neural network model using Python. First, you will create the model using a specific convolutional architecture, and then you will train the model by applying all the concepts you learned in the theoretical part. After training, you will get metrics that will allow you to analyze and evaluate how good your model is. Based on the results, you will learn how to implement changes into your project so that you can learn how to explore different options in order to improve the model performance.

You don’t need previous knowledge on Deep Learning, Convolutional Neural Networks or Pytorch, because you will learn the basics you need in order to build a deep learning project using this technology.

So, get started building convolutional deep learning projects with this very practical course.

Who this course is for
Anyone who wants to learn about how to implement a convolutional neural network using Python and Pytorch
Anyone who wants to learn about how a Deep Learning project is developed