神经网络与深度学习
电子书推荐
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Neural Networks and Deep Learning-神经网络与深度学习-PDF版 评分:
Michael Nielsen所著的Neural Networks and Deep Learning,非常适合用来入门神经网络和深度学习。原书为网页版书籍。这里提供PDF版本书籍。PDF版本制作者:欧拉。欧拉的博客:www.liuhao.me
上传时间:2016-10 大小:13.4MB
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《Neural Networks and Deep Learning》(美)Michael Nielsen 著 英文版.pdf
2019-05-09《Neural Networks and Deep Learning》(美)Michael Nielsen 著 英文版
- 3.56MB
neural-networks-and-deep-learning.pdf
2021-10-05neural-networks-and-deep-learning.pdf
- 5.68MB
Neural networks and deep learning.pdf
2019-07-11这是深度学习的一本入门书籍(英文版),书中理论与实践有机结合,易于上手。
- 3.95MB
Neural Network and Deep Learning-ch(中文版).pdf
2019-07-16神经⽹络是有史以来发明的最优美的编程范式之⼀。在传统的编程⽅法中,我们告诉计算机做什么,把⼤问题分成许多⼩的、精确定义的任务,计算机可以很容易地执⾏。相⽐之下,在神经⽹络中,我们不告诉计算机如何解决我们的问题。相反,它从观测数据中学习,找出它⾃⼰的解决问题的⽅法。 从数据中⾃动学习,听上去很有前途。然⽽,直到2006 年,除了⽤于⼀些特殊的问题,我们仍然不知道如何训练神经⽹络去超越传统的⽅法。2006 年,被称为“深度神经⽹络” 的学习技术的发现引起了变⾰。这些技术现在被称为“深度学习”。它们已被进⼀步发展,今天深度神经⽹络和深度学习在计算机视觉、语⾳识别、⾃然语⾔处理等许多重要问题上都取得了显著的性能。他们正被⾕歌、微软、Facebook 等公司⼤规模部署。 这本书的⽬的是帮助你掌握神经⽹络的核⼼概念,包括现代技术的深度学习。在完成这本书的学习之后,你将使⽤神经⽹络和深度学习来解决复杂模式识别问题。你将为使⽤神经⽹络和深度学习打下基础,来攻坚你⾃⼰设计中碰到的问题。
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Neural Network and Deep Learning_中文版(Michael Nielsen著)
2018-02-13深度学习入门经典,以初学者的角度来讲解,python实现自己的深度学习,另外有很多训练的trick
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Neural Networks and Deep Learning - 神经网络与深度学习 中英双版本
2017-12-13Neural Networks and Deep Learning - 神经网络与深度学习 中英两个版本文件- 完美排版
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Neural Networks and Deep Learning-神经网络与深度学习-zh.zip
2017-12-26Neural Networks and Deep Learning神经网络与深度学习 中文版.pdf 个人收集电子书,仅用学习使用,不可用于商业用途,如有版权问题,请联系删除!
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Neural Networks and Deep Learning--中文翻译
2018-04-20Neural Networks and Deep Learning-神经网络与深度学习,中文翻译版本
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neural-networks-and-deep-learning-master 深度学习与神经网络.zip
2019-05-18neural-networks-and-deep-learning-master 深度学习与神经网络中英文,以及源码2.7,至于3.0本人实践后会在博客更新
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Neural Networks and Deep Learning - Deep Learning explained to your granny
2019-04-23Neural Networks & Deep Learning Deep Learning explained to your granny – A visual introduction for beginners who want to make their own Deep Learning Neural ...神经网络与深度学习的入门书籍方便理解
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中文翻译版Neural Networks and Deep Learning(by Michael Nielsen)& 源码实例
2018-01-02Michael Nielsen的⼀本书兼顾理论和动⼿实践的书。讲解了神经网络和深度学习的众多核心概念,也包含了作者对深度学习的深刻理解和透彻思考,并附代码实例。非常适合初学者入门。
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Neural Networks and Deep Learning.pdf
2020-11-19神经网络和深度学习英文版,Deep Learning explained to your granny – A visual introduction for beginners who want to make their own Deep Learning Neural Network
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"Neural Networks and Deep Learning" by Michael Nielsen 中英文(文字版)
2018-04-23深度学习大牛的权威之作。CNN的第一层通常都是卷积层。你必须记住的第一件事应当是卷积层(conv layer)的输入是什么。就像之前说的,输入是一个32*32*3的像素数列。要解释这个卷积层,最好的办法就是想象一下下面场景:你举着手电筒将光束打在一幅图像的左上角。我们假定这个光束覆盖的范围是5*5。
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Neural Networks and Deep Learning A Textbook 完整版
2019-01-28amazon上评价不错的一本新书,对于深度学习的入门和提高是一本不错的书,值得花时间钻研学习一下。
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Udemy - Deep Learning Recurrent Neural Networks in Python
2017-10-29https://www.udemy.com/deep-learning-recurrent-neural-networks-in-python/ Deep Learning: Recurrent Neural Networks in Python GRU, LSTM, + more modern deep learning, machine learning, and data science for sequences Created by Lazy Programmer Inc. Last updated 5/2017 English What Will I Learn? Understand the simple recurrent unit (Elman unit) Understand the GRU (gated recurrent unit) Understand the LSTM (long short-term memory unit) Write various recurrent networks in Theano Understand backpropagation through time Understand how to mitigate the vanishing gradient problem Solve the XOR and parity problems using a recurrent neural network Use recurrent neural networks for language modeling Use RNNs for generating text, like poetry Visualize word embeddings and look for patterns in word vector representations Requirements Calculus Linear algebra Python, Numpy, Matplotlib Write a neural network in Theano Understand backpropagation Probability (conditional and joint distributions) Write a neural network in Tensorflow Description Like the course I just released on Hidden Markov Models, Recurrent Neural Networks are all about learning sequences – but whereas Markov Models are limited by the Markov assumption, Recurrent Neural Networks are not – and as a result, they are more expressive, and more powerful than anything we’ve seen on tasks that we haven’t made progress on in decades. So what’s going to be in this course and how will it build on the previous neural network courses and Hidden Markov Models? In the first section of the course we are going to add the concept of time to our neural networks. I’ll introduce you to the Simple Recurrent Unit, also known as the Elman unit. We are going to revisit the XOR problem, but we’re going to extend it so that it becomes the parity problem – you’ll see that regular feedforward neural networks will have trouble solving this problem but recurrent networks will work because the key is to treat the input as a sequence. In the next section of the course, we are going to revisit one of the most popular applications of recurrent neural networks – language modeling. You saw when we studied Markov Models that we could do things like generate poetry and it didn’t look too bad. We could even discriminate between 2 different poets just from the sequence of parts-of-speech tags they used. In this course, we are going to extend our language model so that it no longer makes the Markov assumption. Another popular application of neural networks for language is word vectors or word embeddings. The most common technique for this is called Word2Vec, but I’ll show you how recurrent neural networks can also be used for creating word vectors. In the section after, we’ll look at the very popular LSTM, or long short-term memory unit, and the more modern and efficient GRU, or gated recurrent unit, which has been proven to yield comparable performance. We’ll apply these to some more practical problems, such as learning a language model from Wikipedia data and visualizing the word embeddings we get as a result. All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Theano. I am always available to answer your questions and help you along your data science journey. This course focuses on “how to build and understand“, not just “how to use”. Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” via experimentation. It will teach you how to visualize what’s happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. See you in class! NOTES: All the code for this course can be downloaded from my github: /lazyprogrammer/machine_learning_examples In the directory: rnn_class Make sure you always “git pull” so you have the latest version! HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE: calculus linear algebra probability (conditional and joint distributions) Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations, loading a CSV file Deep learning: backpropagation, XOR problem Can write a neural network in Theano and Tensorflow TIPS (for getting through the course): Watch it at 2x. Take handwritten notes. This will drastically increase your ability to retain the information. Write down the equations. If you don’t, I guarantee it will just look like gibberish. Ask lots of questions on the discussion board. The more the better! Realize that most exercises will take you days or weeks to complete. Write code yourself, don’t just sit there and look at my code. USEFUL COURSE ORDERING: (The Numpy Stack in Python) Linear Regression in Python Logistic Regression in Python (Supervised Machine Learning in Python) (Bayesian Machine Learning in Python: A/B Testing) Deep Learning in Python Practical Deep Learning in Theano and TensorFlow (Supervised Machine Learning in Python 2: Ensemble Methods) Convolutional Neural Networks in Python (Easy NLP) (Cluster Analysis and Unsupervised Machine Learning) Unsupervised Deep Learning (Hidden Markov Models) Recurrent Neural Networks in Python Artificial Intelligence: Reinforcement Learning in Python Natural Language Processing with Deep Learning in Python Who is the target audience? If you want to level up with deep learning, take this course. If you are a student or professional who wants to apply deep learning to time series or sequence data, take this course. If you want to learn about word embeddings and language modeling, take this course. If you want to improve the performance you got with Hidden Markov Models, take this course. If you’re interested the techniques that led to new developments in machine translation, take this course. If you have no idea about deep learning, don’t take this course, take the prerequisites.
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Neural Networks and Deep Learning-2018_Neuralnetworks_神经网络书_深度神经
2021-09-11很好地关于神经网络和深度学的书,我一直在学,很有用
- 12.95MB
Neural Networks and Deep Learning神经网络与深度学习.zip
2019-07-17Neural Networks and Deep Learning神经网络与深度学习 Neural Networks and Deep Learning神经网络与深度学习
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神经网络和深度学习neural networks and deep-learning-zh.pdf
2021-08-22神经网络和深度学习
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《Neural Networks and Deep Learning》中文版
2018-11-14非常清晰非常清晰!!!Michael Nielsen 大神的 《Neural Networks and Deep Learning》 网络教程一直是很多如我一样的小白入门深度学习的很好的一本初级教程。不过其原版为英文,对于初期来说我们应该以了解原理和基本用法为主,所以中文版其实更适合初学者。幸好国内有不少同好辛苦翻译了一个不错的中文版本,并且使用 LaTex 进行排版以方便阅读。
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neural-networks-and-deep-learning书籍源代码
2018-10-04neural-networks-and-deep-learning书籍的一些demo,使用python2编写,有需要可以下载
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neural networks and deep learning 神经网络与深度学习
2019-02-21《神经网络与深度学习》- Michael Nielsen 神经网络是有史以来最美丽的编程模型之一。在传统的编程方法中,我们告诉计算机该做什么,将大问题分解成许多小的、精确定义的任务,计算机可以轻松执行。相比之下,在神经网络中,我们不告诉计算机如何解决我们的问题。相反,它从观测数据中学习,找出自己的解决方案。从数据中自动学习听起来很有希望。然而,直到2006年,我们才知道如何训练深度神经网络超越更传统的方法,除了一些特殊的问题。2006年发生的变化是在产生了所谓的深度神经网络相关的技术。这些技术现在被称为深度学习。 它们得到了进一步的发展,今天,深度神经网络和深度学习在计算机视觉、语音识别和自然语言处理的许多重要问题上取得了杰出的表现。谷歌、微软和Facebook等公司正在大规模部署它们。这本书的目的是帮助你掌握神经网络的核心概念,包括深度学习的现代技术。读完这本书后,你将会编写出使用神经网络和深度学习来解决复杂模式识别问题的代码。你将有一个基础来利用神经网络和深度学习来解决你面临的问题。
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Neural Networks and Deep Learning中文版
2018-04-05原英文网站:http://neuralnetworksanddeeplearning.com/ 中文翻译:https://www.gitbook.com/book/hit-scir/neural-networks-and-deep-learning-zh_cn/details 方便无梯子的同学:链接: https://pan.baidu.com/s/12UVkF4M1WUPAvZC28BH0vA 密码: hq7x
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Neural Networks and Deep Learning
2018-04-20Michael Nielsen's book,Neural networks, a beautiful biologically-inspired,programming paradigm which enables a computer to learn from observational data
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Neural_Networks_-_A_Comprehensive_Foundation_-_Simon_Haykin.pdf
2018-03-15A very good textbook for researchers working on the NN theory, yet if you just want to know what is NN, it perhaps is too much for you to digest!
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neural networks and deep learning --michael nielsen
2017-07-24michael nielsen 的neural networks and deep learning ,very nice!
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neural networks and deep learning
2016-08-02neural networks and deep learning完整版,对神经网络的学习有很大帮助
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python大作业 含爬虫、数据可视化、地图、报告、及源码(整和为一个文件)(2014-2020全国各地区原油加工量).rar
2021-12-03(含源码及报告)本程序分析了自2014年到2020年每年我国原油加工的产量,并且分析了2019年全国各地区原油加工量,含饼状图,柱状图,折线图,数据在地图上显示。运行本程序需要requests、bs4、csv、pandas、matplotlib、pyecharts库的支持,如果缺少某库请自行安装后再运行。文件含2个excel表,4个csv文件以及一个名字为render的html文件(需要用浏览器打开),直观的数据处理部分是图片以及html文件,数据处理的是excel文件。不懂可以扫文件中二维码在微信里面问。
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仿真电路以及操作方法
2020-12-20用一片通用四运放芯片LM324组成电路,实现以下功能:用低频信号源产生ui1=0.1sin2πft(V),f=500Hz的正弦波信号,加至加法器的输入端,加法器的另输入端加入有自制振荡器产生的信号uo1。要求加法器的输出电压ui2=10 ui1+ uo1。ui2经选频滤波器滤除uo1频率分量,选出f信号为uo2,uo2为峰峰值等于9V的正弦信号。uo2信号经比较器后在1KΩ负载上得到峰峰值2V的输出电压uo3。用NI Multisim 打开即可,参数已调好。对应博客:https://blog.csdn.net/weixin_43723423/article/details/90761331
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【纯干货啊】华为IPD流程管理(完整版).pptx
2020-06-01华为IPD产品研发流程完整版。非常的详细,很适合给新是的实习生做培训用!是我一直在用的流程管理,很适合学习与交流。
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可编程语言标准IEC61131-3中文版.pdf
2022-01-09可编程语言标准IEC61131-3中文版