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[其他教程] Deep Dive into Python Machine Learning (2016)

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发表于 2017-1-6 16:50:39 | 显示全部楼层 |阅读模式
深入Python机器学习(2016)
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MP4 | AVC 116kbps |英语| 1280x720 | 25fps | 1h 45mins | AAC立体声141kbps | 2.64 GB
类型:视频培训
深度学习是目前在图像识别,语音识别,对象识别和自然语言问题方面最好的解决方案提供商之一,它提供了越来越多的Python库。深度学习的目的是通过增加和改进每个网络的训练层的数量来开发深度神经网络,使得机器更多地了解数据,直到它尽可能准确。开发人员可以利用深度学习提供的技术来完成复杂的机器学习任务,并训练人工智能网络以开发更深层次的感知识别。

深度学习是使用更高级实现的机器学习的下一步。目前,它不是作为一个行业标准,而是朝着这个方向前进,带来了一个强大的承诺,在处理原始非结构化数据时,是一个改变游戏规则。深度学习目前是关于图像识别,语音识别,对象识别和自然语言处理中的问题的解决方案的最佳提供者之一。开发人员可以利用构建AI程序的好处,而不是使用手工编码规则,从示例中学习如何解决复杂的任务。通过深入学习被许多数据科学家使用,更深的神经网络被评估以获得准确的结果。

本课程将介绍从基础微积分知识到理解反向传播及其在神经网络中用于深度学习和理解自动分化的应用。通过课程,我们将涵盖卷积,复发神经网络的彻底训练,并建立重点监督学习的理论,并整合到您的产品,如搜索,图像识别和对象处理。此外,我们将检查情感分析模型的性能,并将结束与Tensorflow的介绍。

在本课程结束时,您可以立即开始深入学习。本课程将使您对您在当前工作中的实施以及进一步的研究有信心。

风格和方法
一个易于遵循和结构化的视频教程,带有IPython笔记本的实用示例和编码,以帮助您掌握深度学习的每一个方面。
Deep Dive into Python Machine Learning (2016)

MP4 | AVC 116kbps | English | 1280x720 | 25fps | 1h 45mins | AAC stereo 141kbps | 2.64 GB
Genre: Video Training
Deep learning is currently one of the best providers of solutions regarding problems in image recognition, speech recognition, object recognition, and natural language with its increasing number of libraries that are available in Python. The aim of deep learning is to develop deep neural networks by increasing and improving the number of training layers for each network, so that a machine learns more about the data until it’s as accurate as possible. Developers can avail the techniques provided by deep learning to accomplish complex machine learning tasks, and train AI networks to develop deep levels of perceptual recognition.

Deep learning is the next step to machine learning with a more advanced implementation. Currently, it’s not established as an industry standard, but is heading in that direction and brings a strong promise of being a game changer when dealing with raw unstructured data. Deep learning is currently one of the best providers of solutions regarding problems in image recognition, speech recognition, object recognition, and natural language processing. Developers can avail the benefits of building AI programs that, instead of using hand coded rules, learn from examples how to solve complicated tasks. With deep learning being used by many data scientists, deeper neural networks are evaluated for accurate results.

This course takes you from basic calculus knowledge to understanding backpropagation and its application for training in neural networks for deep learning and understand automatic differentiation. Through the course, we will cover thorough training in convolutional, recurrent neural networks and build up the theory that focuses on supervised learning and integrate into your product offerings such as search, image recognition, and object processing. Also, we will examine the performance of the sentimental analysis model and will conclude with the introduction of Tensorflow.

By the end of this course, you can start working with deep learning right away. This course will make you confident about its implementation in your current work as well as further research.

Style and Approach
An easy-to-follow and structured video tutorial with practical examples and coding with IPython notebooks to help you get to grips with each and every aspect of deep learning.
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发表于 2017-1-6 16:50:39 | 显示全部楼层
真是被感动的痛哭流涕……
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发表于 2017-2-10 20:41:07 | 显示全部楼层
学了  先看看教程咋样
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发表于 2017-3-5 01:02:32 | 显示全部楼层
牛牛  牛逼教程
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发表于 2017-3-10 10:10:00 | 显示全部楼层
楼主爱 你  这教程来得及时
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发表于 2017-4-18 15:41:46 | 显示全部楼层
教程太棒了  感谢分享
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发表于 2017-6-7 18:46:26 | 显示全部楼层
好东西支持支持再支持
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发表于 2017-7-19 21:00:00 | 显示全部楼层
学了  先看看教程咋样
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发表于 2017-9-5 22:50:00 | 显示全部楼层
楼主爱 你  这教程来得及时
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