神经网络与深度学习（ Neural Networks and Deep Learning）

最新推荐文章于 2023-01-18 11:05:50 发布
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博客给出了一个网址http://neuralnetworksanddeeplearning.com/ ，推测与神经网络和深度学习相关。

http://neuralnetworksanddeeplearning.com/

《神经⽹络与深度学习》（Neural Networks and Deep Learning）
06-11
（美）Michael Nielsen 著，Neural+Networks+and+Deep+Learning-神经网络与深度学习.pdf
Neural Networks and Deep Learning - 神经网络与深度学习 中英双版本
12-13
深度学习是神经网络的一个分支，它主要涉及多层的神经网络结构，也称为深层神经网络（Deep Neural Network, DNN）。这些层级允许网络逐渐学习更复杂的特征，从原始输入数据中提取高层次的信息。深度学习在图像识别、...
深度学习笔记（一）——神经网络和深度学习(Neural Networks and Deep Learning)
HJ的博客
03-20 1544
什么是神经网络？(What is a Neural Network) 我们常常用深度学习这个术语来指训练神经网络的过程。有时它指的是特别大规模的神经网络训练。那么神经网络究竟是什么呢？在这个视频中，我会讲解一些直观的基础知识。 让我们从一个房价预测的例子开始讲起。 假设你有一个数据集，它包含了六栋房子的信息。所以，你知道房屋的面积是多少平方英尺或者平方米，并且知道房屋价格。这时，你想要拟合一个根据...
神经网络与深度学习（Neural Networks and Deep Learning）
weixin_42989738的博客
06-27 743
1.什么是神经网络 感知器 1.2 一个简单的分类手写数字网络
Neural Networks and Deep Learning（神经网络与深度学习）_On the exercises and problems
fenghuashumidiexiang的专栏
01-15 284
技术书籍包括作者的警告（admonition），读者必须做练习和问题，这并不罕见。当我读到这样的警告（warning）时，总是感到有些奇怪（peculiar）。如果我不做练习和问题，我会有什么不好的事情发生吗？当然不是。我会得到一些时间，但代价是加深理解。有时候这是值得的。有时候不是。 那么这本书有什么值得做的呢？我的建议是，你真的应该尝试（attempt）大部分的练习，你应该以不做大部分问
神经网络和深度学习(Neural Networks and Deep Learning)
weixin_38353277的博客
01-18 949
神经网络和深度学习
神经网络和深度学习neural networks and deep-learning-zh.pdf
08-22
神经网络和深度学习
神经⽹络与深度学习 Neural Networks and Deep Learning （美）Michael Nielsen 著
05-07
《神经网络与深度学习》是Michael Nielsen撰写的一本经典著作，深入浅出地介绍了神经网络和深度学习领域的核心概念和技术。这本书不仅包含了丰富的理论知识，还提供了配套的代码，使得读者能够更好地理解和实践所学...
Neural Networks and Deep Learning.神经网络与深度学习
03-13
Michael Nielsen的Neural Networks and Deep Learning，由Xiaohu Zhu，Freeman Zhang等人提供中文翻译的开源版本，这个是最新的v0.5中文版。
neural networks and deep learning 神经网络与深度学习
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...
neural-networks-and-deep-learning-master 深度学习与神经网络.zip
05-18
neural-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
Neural Networks and Deep Learning A Textbook 完整版
01-28
amazon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。
《Neural Networks and Deep Learning》中文版
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。
Udemy - Deep Learning Recurrent Neural Networks in Python
10-29
https://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.
《Neural Network and Deep Learning（神经网络与深度学习）》练习及问题详解
Champagne Jin
09-30 2582
1.2 练习 第一部分 我们先从一个感知器入手，设一个感知器具有nnn个输入xix_ixi​，每个输入的权重分别为wiw_iwi​，偏置为bbb，由此可以得到的计算结果为∑i=1nxiwi+b\sum_{i=1}^{n} x_iw_i + b∑i=1n​xi​wi​+b，而在输出层所要做的是将该结果与0进行大小比较，若∑i=1nxiwi+b⩾0\sum_{i=1}^{n} x_iw_i + b...
NEURAL NETWORKS（neural networks and deep learning by Charu C. Aggarwa）
qq_40590753的博客
01-22 383
1. introduction
深度学习--神经网络Neural Network and Deep Learning
xusenquan的博客
06-07 297
一：两种神经网络 感知型和S型，权重w和偏置b 感知型规则模型： 实际上，⽹络中单个感知器上⼀个权重或 偏置的微⼩改动有时候会引起那个感知器的输 出完 全翻转，如0变到1 S型： S型神经元有多个输⼊，x1,x2,…。但是这些输⼊可以取0和1中的任意 值，⽽不仅仅是0或1。 二 神经网络架构 输入层，隐藏层，输出层，， 多次感知器MLP 三 梯度下降学习法： ⼀直使⽤它。 重复⼀下，我们训练神经⽹络的⽬的是找到能最⼩化⼆次代价函数C(w,b)的权重和偏置。 为了计算梯度∇
《Neural Networks and Deep Learning》深度学习与神经网络权威指南
资源摘要信息:"Neural Networks and Deep Learning-2018_Neuralnetworks_神经网络书_深度神经" 知识点详细说明： 1. 神经网络与深度学习基础 - 神经网络是深度学习的基础，它是一种模仿人脑神经元结构的计算模型。...
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