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Coursera – Neural Networks for Machine Learning

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 Coursera - Neural Networks for Machine LearningCoursera - Neural Networks for Machine Learning
MP4 | AVC 29kbps | English | 960x540 | 15fps | 16h 26mins | AAC stereo 128kbps | 920 MB
Genre: Video Training

Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well. Neural networks use learning algorithms that are inspired by our understanding of how the brain learns, but they are evaluated by how well they work for practical applications such as speech recognition, object recognition, image retrieval and the ability to recommend products that a user will like. As computers become more powerful, Neural Networks are gradually taking over from simpler Machine Learning methods.

They are already at the heart of a new generation of speech recognition devices and they are beginning to outperform earlier systems for recognizing objects in images. The course will explain the new learning procedures that are responsible for these advances, including effective new proceduresr for learning multiple layers of non-linear features, and give you the skills and understanding required to apply these procedures in many other domains.

Lecture 1: Introduction

Lecture 2: The Perceptron learning procedure

Lecture 3: The backpropagation learning proccedure

Lecture 4: Learning feature vectors for words

Lecture 5: Object recognition with neural nets

Lecture 6: Optimization: How to make the learning go faster

Lecture 7: Recurrent neural networks

Lecture 8: More recurrent neural networks

Lecture 9: Ways to make neural networks generalize better

Lecture 10: Combining multiple neural networks to improve generalization

Lecture 11: Hopfield nets and Boltzmann machines

Lecture 12: Restricted Boltzmann machines (RBMs)

Lecture 13: Stacking RBMs to make Deep Belief Nets

Lecture 14: Deep neural nets with generative pre-training

Lecture 15: Modeling hierarchical structure with neural nets

Lecture 16: Recent applications of deep neural nets


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  1. Coursera – 用于机器学习的神经网络 时长:16h 26mins 视频格式:MP4 | AVC 29kbps | English | 960×540 | 15fps 音频格式:AAC stereo 128kbps 发行日期:2014.2.28 语言:英语 容量:920mb 学习人工神经网络以及如何用于机器学习,作为用于语音和对象识别、图形分割、语言和人类动作建模等。我们会既强调基本算法,也会重视实际工作中的技巧。神经网络使用我们对大脑如何工作的成果学习算法,但是他们通过实际的应用程序,如语音识别、对象识别、图像检索以及用户喜欢的推荐产品功能。随着计算机变得更强大,神经网络整的机器学习方法逐渐变得更加简单。 第一讲:导论 第二讲:学习过程的认知 第三讲:学习过程的反向传播 第四讲:词语的学习功能向量 第五讲:神经wanglde对象识别 第六讲:优化:如何让学习更快 第七讲:周期性神经网络 第八讲:更多的周期性神经网络 第九讲:让神经网络生成更快的方法 第十讲:合并多个神经网络以改善生成 第十一讲: 霍普菲尔德网络与玻尔兹曼机 第十二讲:受限制的玻尔兹曼机(RBM) 第十三讲:堆叠RBM以更深度地Belief网络 第十四讲:使用generative pre-training的深度神经网络 第十五讲:使用神经网络建模层级结构 第十六讲:进来深度神经网络的应用
    wilde(特殊组-翻译)3年前 (2014-03-05)