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Deep Learning with PyTorch

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Deep Learning with PyTorch
Deep Learning with PyTorch
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 4 Hours 42M | 932 MB
Genre: eLearning | Language: English

This video course will get you up-and-running with one of the most cutting-edge deep learning libraries: PyTorch. Written in Python, PyTorch 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.

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.

Deep Learning with PyTorch

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  1. PyTorch深度学习 本教程将会帮你开始使用最先进的深度学习类库:PyTorch。由于其比其他类库易于使用以及其在动态计算图像方面的使用,PyTorch正在吸引着所有数据科学专家的目光。 在本教程中,你将学习如何使用卷积神经网络处理如图片的空间坐标数据,并使用递归神经网络处理如文本一类的序列化数据,从而实现几个有用的任务。你将学习如何使用AutoEncoders制作非标签化数据。你还将使用增强学习训练神经网络学习如何平衡。通过本教程,你将使用PyTorch框架的各种机制完成这些任务。 学习完本教程,你将很好滴理解所用到的算法和技术。掌握PyTorch的知识并应用在每天的机器学习问题上。
    wilde(特殊组-翻译)3周前 (05-07)