神经网络(2)---neurons and the brain

神经网络是一个历史悠久的课题,当初提出是为了让机器能够模仿人的大脑一样工作.现在神经网络在处理很多机器学习的问题上发挥了很重要的作用. 神经网络的背景(我们可以利用神经网络做些什么) 大脑可以处理视觉,听觉,计算,触觉等等很多事情,这样看来似乎如果我们要模仿大脑的话,要写很多不同的程序来让机器模仿人脑能做的这些事情,但是有个设想是大脑做所有的这些不同的事情并不是通过各种不同的程序来的,实际上大脑是通过just a single learning algorithm.这只是一个设想,让我们看看一些…

Coursera《machine learning》--（8）神经网络表述

本笔记为Coursera在线课程<Machine Learning>中的神经网络章节的笔记. 八.神经网络:表述(Neural Networks: Representation) 本节主要讨论一种叫做神经网络的机器学习算法.首先讨论神经网络的表层结构,在后续的课程中再讨论具体的学习算法.神经网络其实是一个比较古老的算法,它沉寂过一点时间,但现在又成为了许多机器学习的首选技术. 8.1 非线性假设参考视频: 8 - 1 - Non-linear Hypotheses (10 min).mkv…

【原】Coursera—Andrew Ng机器学习—课程笔记 Lecture 8_Neural Networks Representation 神经网络的表述

神经网络是一种受大脑工作原理启发的模式. 它在许多应用中广泛使用:当您的手机解释并理解您的语音命令时,很可能是神经网络正在帮助理解您的语音; 当您兑现支票时,自动读取数字的机器也使用神经网络. 8.1 非线性假设 Non-linear Classification 参考视频 : 8 - 1 - Non-linear Hypotheses (10 min).mkv 线性回归和逻辑回归的缺点: 当输入数据特征过多,计算负荷大. 计算机视觉中,图片的表示是通过像素矩阵表示的.假设一个图片是50×50p…

斯坦福机器学习视频笔记 Week4 & Week5 神经网络 Neural Networks

神经网络是一种受大脑工作原理启发的模式. 它在许多应用中广泛使用:当您的手机解释并理解您的语音命令时,很可能是神经网络正在帮助理解您的语音; 当您兑现支票时,自动读取数字的机器也使用神经网络. Non-linear Classification 当输入数据特征过多,像上面的例子,当使用三次幂的特征时,可以超过170,000项,使我们的逻辑回归难以运行. 还有在计算机视觉中,图片的表示是通过像素矩阵表示的,如上图所示.那么假设一个图片是简单的50×50px,其特征数为2500(7500 if RG…

Can brain stimulation aid memory and brain health?

Can brain stimulation aid memory and brain health? Harvard Women’s Health Watch Image: Thinkstock The jury is still out, but magnetic fields or mild electric currents may aid learning and help several conditions that affect women in particular. Stimu…

The Brain vs Deep Learning Part I: Computational Complexity — Or Why the Singularity Is Nowhere Near

The Brain vs Deep Learning Part I: Computational Complexity — Or Why the Singularity Is Nowhere Near July 27, 2015July 27, 2015 Tim Dettmers Deep Learning, NeuroscienceDeep Learning, dendritic spikes, high performance computing, neuroscience, singula…

Advances in Single Cell Genomics to Study Brain Cell Types | 会议概览

单细胞在脑科学方面的应用 Session 1: Deciphering the Cellular Landscape of the Brain Using Single Cell Transcriptomics Single cell/nucleus transcriptomics has emerged as a powerful approach to classify cell types and dynamic cell states in any multicellular organ…

TED_Topic8：How to control someone else's arm with your brain

By Greg Gage (Neuroscientist) Greg Gage is on a mission to make brain science accessible to all. In this fun, kind of creepy(令人毛骨悚然的,恐怖的) demo, the neuroscientist and TED Senior Fellow uses a simple, inexpensive DIY kit to take away the free will of…

Machine Learning - 第4周（Neural Networks: Representation）

Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets(解释口译) and understand your voice commands, it is likely that a neural network is helping to understand your speech; wh…

机器学习之神经网络模型-上（Neural Networks: Representation）

在这篇文章中,我们一起来讨论一种叫作"神经网络"(Neural Network)的机器学习算法,这也是我硕士阶段的研究方向.我们将首先讨论神经网络的表层结构,在之后再具体讨论神经网络学习算法. 神经网络实际上是一个相对古老的算法,并且沉寂了一段时间,不过到了现在它又成为许多机器学习问题的首选技术. 1. Non-linear Hypotheses 之前我们已经介绍过线性回归和逻辑回归算法了,那为什么还要研究神经网络? 为了阐述研究神经网络算法的目的,我们首先来看几个机器学习问题作为例子…

Machine Learning - week 4 - Non-linear Hypotheses

为什么计算机图像识别很难呢?因为我们看到的是汽车,而计算机看到的是表示颜色的 RGB 数值.计算机需要根据这些数值来判断. 如果图片是 50 * 50 像素,那么一共有 2500 个像素点.如果是 Quadratic features,那么是 xi, xj 的组合有 2500 + 2499 + ... + 1 约为 300 百万个. Neurons and the Brain 大脑可以学习很多种算法,但是程序是固定的(出生到死亡都没有人修改你的大脑程序).通过计算机模仿大脑的这种学习,这样,就不…

Teaching Machines to Understand Us 让机器理解我们之二深度学习的历史

Deep history 深度学习的历史 The roots of deep learning reach back further than LeCun’s time at Bell Labs. He and a few others who pioneered the technique were actually resuscitating a long-dead idea in artificial intelligence. 深度学习的研究之根是在LeCun在Bell实验室研究之前的.…

吴恩达-coursera-机器学习-week4

第八.神经网络:表述(Neural Networks: Representation) 8.1 非线性假设 8.2 神经元和大脑 8.3 模型表示1 8.4 模型表示2 8.5 样本和直观理解1 8.6 样本和直观理解II 8.7 多类分类第4周第八.神经网络:表述(Neural Networks: Representation) 8.1 非线性假设参考视频: 8 - 1 - Non-linear Hypotheses (10 min).mkv 我们之前学的,无论是线性回归还是逻辑回归都有…

Machine learning （8-Neural Networks: Representation）

1.Non-linear Hypotheses 2.Neurons and the Brain 从某种意义上来说,如果我们能找出大脑的学习算法,然后在计算机上执行大脑学习算法或与之相似的算法,也许这将是我们向人工智能迈进做出的最好的尝试.人工智能的梦想就是:有一天能制造出真正的智能机器. 3.Model Representation I 第一层成为输入层(Input Layer),最后一层称为输出层(Output Layer),中间一层成为隐藏层(Hidden Layers).我们为每一层都增…

（转） Artificial intelligence, revealed

Artificial intelligence, revealed Yann LeCunJoaquin Quiñonero Candela It's 8:00 am on a Tuesday morning. You've awoken, scanned the headlines on your phone, responded to an online post, ordered a holiday sweater for your mom, locked up the house, and…

（转）WHY DEEP LEARNING IS SUDDENLY CHANGING YOUR LIFE

Main Menu Fortune.com E-mail Tweet Facebook Linkedin Share icons By Roger Parloff Illustration by Justin Metz SEPTEMBER 28, 2016, 5:00 PM EDT WHY DEEP LEARNING IS SUDDENLY CHANGING YOUR LIFE Decades-old discoveries are now electrifying the comp…

（转）The Road to TensorFlow

Stephen Smith's Blog All things Sage 300… The Road to TensorFlow – Part 7: Finally Some Code leave a comment » Introduction Well after a long journey through Linux, Python, Python Libraries, the Stock Market, an Introduction to Neural Networks and tr…

CN_Week1_Receptive_Field

0. The introduction: 1. An example: Models of "Receptive Fields". 2. An intuitive method of showing the electrical activities of neurons -- converting them to audio signals. 1. Descriptive Model of Receptive Field Maybe it's a center-surrond RF:…

Neural Networks and Deep Learning

Neural Networks and Deep Learning This is the first course of the deep learning specialization at Coursera which is moderated by moderated by DeepLearning.ai. The course is taught by Andrew Ng. Introduction to deep learning Be able to explain the maj…

[C1] Andrew Ng - AI For Everyone

About this Course AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone--especially your non-technical colleagues--to take. In this course, you will learn: The meaning behind com…

[C2P2] Andrew Ng - Machine Learning

##Linear Regression with One Variable Linear regression predicts a real-valued output based on an input value. We discuss the application of linear regression to housing price prediction, present the notion of a cost function, and introduce the gradi…

Evolutionary approaches towards AI: past, present, and future

Evolutionary approaches towards AI: past, present, and future 2019-10-06 07:28:13 This blog is from: https://towardsdatascience.com/evolutionary-approaches-towards-ai-past-present-and-future-b23ccb424e98 Table of Contents Introduction Genetics and na…

用Google Brain的机器学习项目：Magenta，教神经网络学抖音小姐姐作曲。

先上我们要学习的小姐姐的美照.. 一.配置环境 1.自己配置环境:python,tensorflow,bazel(编译),java.然后下载magenta(https://github.com/tensorflow/magenta.git,进入magenta目录) 2.docker集成环境:tensorflow/magenta(我这里用的集成环境,magenta已经下载,进入magenta目录) 二.创建旋律数据库 1.下载抖音视频到本地,通过格式工厂转换为MP3音频文件 2.通过WIDI把M…

【转】Artificial Neurons and Single-Layer Neural Networks

原文:written by Sebastian Raschka on March 14, 2015 中文版译文:伯乐在线 - atmanic 翻译,toolate 校稿 This article offers a brief glimpse of the history and basic concepts of machine learning. We will take a look at the first algorithmically described neural network…

Stanford机器学习---第四讲. 神经网络的表示 Neural Networks representation

原文 http://blog.csdn.net/abcjennifer/article/details/7749309 本栏目(Machine learning)包括单参数的线性回归.多参数的线性回归.Octave Tutorial.Logistic Regression.Regularization.神经网络.机器学习系统设计.SVM(Support Vector Machines 支持向量机).聚类.降维.异常检测.大规模机器学习等章节.所有内容均来自Standford公开课machine…

《转》循环神经网络(RNN, Recurrent Neural Networks)学习笔记：基础理论

转自 http://blog.csdn.net/xingzhedai/article/details/53144126 更多参考:http://blog.csdn.net/mafeiyu80/article/details/51446558 http://blog.csdn.net/caimouse/article/details/70225998 http://kubicode.me/2017/05/15/Deep%20Learning/Understanding-about-RNN/ RNN…

基于神经网络的混合计算(DNC)-Hybrid computing using a NN with dynamic external memory

前言: DNC可以称为NTM的进一步发展,希望先看看这篇译文,关于NTM的译文:人工机器-NTM-Neutral Turing Machine 基于神经网络的混合计算 Hybrid computing using a neural network with dynamic external memory 原文:Nature:doi: 10.1038/nature20101 异义祠:memory matrix :存储矩阵,内存以矩阵方式编码,亦成为记忆矩阵. the neural Turing m…