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所在平台: Udemy |
课程主页: https://www.udemy.com/course/pytorch-deep-learning-with-pytorch-masterclass-2-in-1/
课程评论：没有评论
课程名称：PyTorch：深度学习与PyTorch大师班：2合1 课程概述：PyTorch是一个用Python编写的深度学习框架，因其易用性和动态计算图的特性，受到所有数据科学专业人士的关注。它支持图形处理单元（GPU），为研究人员和数据科学家提供了最大灵活性和速度。通过PyTorch，您能够动态构建神经网络，并轻松执行高级人工智能任务。本综合性的2合1课程采用实用方法，结合真实世界的案例，帮助您使用PyTorch创建自己的应用程序！ 课程内容包括两个完整的部分，旨在提供最全面的培训。第一部分是《使用PyTorch进行深度学习》，主要涵盖利用PyTorch深度学习框架构建有效模型的内容。您将学习如何使用卷积神经网络（CNN）处理图像等空间数据，以及使用递归神经网络（RNN）处理文本等序列数据。此外，您还将探索如何使用自编码器利用未标记数据。课程还将介绍强化学习，通过训练神经网络自主平衡杆子的技巧。通过此次学习，您将掌握PyTorch框架的各种机制，并对算法和技术有良好的理解。 第二部分是《PyTorch深度学习项目》，主要通过真实世界的案例指导您创建深度学习模型。课程从PyTorch的基础开始，接着学习卷积神经网络在图像识别中的应用，并逐步介绍递归神经网络（RNN）和长短期记忆网络（LSTM）在字符序列预测中的应用。您还将了解到使用自编码器检测信用卡欺诈的方法。同时，课程中还将开发一个基于Boltzmann Machines的系统，以推荐观看电影。通过实践项目的实施，您将能够开始使用PyTorch构建深度学习模型。 关于作者：Anand Sahai是一位拥有15年企业产品与服务开发经验的软件专业人士。他在2007年开始与机器学习相关的工作，并于2017年全职专注于深度学习。Ashish Singh Bhatia则是一位具有10年不同领域IT经验的开发者，对Python、Java、R等技术充满热情。 通过本课程，您将全面掌握PyTorch，构建有效的深度学习模型，并通过真实世界的项目提升技能！
PyTorch: written in Python, is grabbing the attention of all data science professionals due to its ease of use over other libraries and its use of dynamic computation graphs. PyTorch is a Deep Learning framework that is a boon for researchers and data scientists. It supports Graphic Processing Units and is a platform that provides maximum flexibility and speed. With PyTorch, you can dynamically build neural networks and easily perform advanced Artificial Intelligence tasks.This comprehensive 2-in-1 course takes a practical approach and is filled with real-world examples to help you create your own application using PyTorch! Begin with exploring PyTorch and the impact it has made on Deep Learning. Design and implement powerful neural networks to solve some impressive problems in a step-by-step manner. Build a Convolutional Neural Network (CNN) for image recognition. Also, predict share prices with Recurrent Neural Network and Long Short-Term Memory Network (LSTM). You'll learn how to detect credit card fraud with autoencoders and much more! By the end of the course, you'll conquer the world of PyTorch to build useful and effective Deep Learning models with the PyTorch Deep Learning framework with the help of real-world examples!Contents and OverviewThis training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Deep Learning with PyTorch, covers building useful and effective deep learning models with the PyTorch Deep Learning framework. In this course, you will learn how to accomplish useful tasks using Convolutional Neural Networks to process spatial data such as images and using Recurrent Neural Networks to process sequential data such as texts. You will explore how you can make use of unlabeled data using Auto-Encoders. You will also be training a neural network to learn how to balance a pole all by itself, using Reinforcement Learning. Throughout this journey, you will implement various mechanisms of the PyTorch framework to do these tasks. By the end of the video course, you will have developed a good understanding of, and feeling for, the algorithms and techniques used. You'll have a good knowledge of how PyTorch works and how you can use it in to solve your daily machine learning problems.The second course, Deep Learning Projects with PyTorch, covers creating deep learning models with the help of real-world examples. The course starts with the fundamentals of PyTorch and how to use basic commands. Next, you'll learn about Convolutional Neural Networks (CNN) through an example of image recognition, where you'll look into images from a machine perspective. The next project shows you how to predict character sequence using Recurrent Neural Networks (RNN) and Long Short Term Memory Network (LSTM). Then you'll learn to work with autoencoders to detect credit card fraud. After that, it's time to develop a system using Boltzmann Machines, where you'll recommend whether to watch a movie or not. By the end of the course, you'll be able to start using PyTorch to build Deep Learning models by implementing practical projects in the real world. So, grab this course as it will take you through interesting real-world projects to train your first neural nets.By the end of the course, you'll conquer the world of PyTorch to build useful and effective Deep Learning models with the PyTorch Deep Learning framework!About the AuthorsAnandSahais a software professional with 15 years' experience in developing enterprise products and services. Back in 2007, he worked with machine learning to predict call patterns at TATA Communications. At Symantec and Veritas, he worked on various features of an enterprise backup product used by Fortune 500 companies. Along the way, he nurtured his interests in Deep Learning by attending Coursera and Udacity MOOCs. He is passionate about Deep Learning and its applications; so much so that he quit Veritas at the beginning of 2017 to focus full time on Deep Learning practices. Anand built pipelines to detect and count endangered species from aerial images, trained a robotic arm to pick and place objects, and implemented NIPS papers. His interests lie in computer vision and model optimization.AshishSingh Bhatia is a learner, reader, seeker, and developer at the core. He has over 10 years of IT experience in different domains, including banking, ERP, and education. He is persistently passionate about Python, Java, R, and web and mobile development. He is always ready to explore new technologies.
