Deep Learning v0.1 documentation

深度学习教程精要
最新推荐文章于 2024-09-23 23:50:52 发布
翻译 最新推荐文章于 2024-09-23 23:50:52 发布 · 880 阅读 · 0

本文档为深度学习初学者提供了一系列教程，涵盖从基础到进阶的内容，包括使用逻辑回归进行MNIST手写数字分类、多层感知机的构建与训练、卷积神经网络（LeNet）的设计及应用，以及堆叠降噪自编码器（SdA）的实现等。通过这些教程，读者可以系统地掌握深度学习的关键技术和实践技巧。
强化学习入门：从Q-learning到Deep Q Network
AI天才研究院
04-12 744
本文旨在为读者提供强化学习的基础知识和实践指导，特别关注从传统Q-learning到深度强化学习的演进过程。内容涵盖强化学习的理论基础、算法实现和实际应用，适合希望系统学习强化学习的开发者和研究人员。文章首先介绍强化学习的基本概念，然后详细讲解Q-learning算法及其数学基础。接着探讨如何将深度学习与Q-learning结合形成DQN，并提供完整的代码实现。最后讨论实际应用场景和未来发展方向。强化学习(Reinforcement Learning)：一种通过与环境交互学习最优行为的机器学习范式。
Springboot 整合 Java DL4J 打造文本摘要生成系统
在技术的广袤天地里，本博客如精准罗盘。剖析前沿科技，深掘代码奥秘，以精炼笔触，带您穿越复杂技术迷宫，速达知识彼岸。
11-09 6433
在信息爆炸的时代，大量的文本数据充斥着我们的生活。无论是新闻报道、学术论文还是各类文档，阅读和理解这些长篇文本都需要耗费大量的时间和精力。为了解决这个问题，文本摘要生成技术应运而生。本文将介绍如何使用 Spring Boot 整合 Java Deeplearning4j 来构建一个文本摘要生成系统，该系统能够自动从长篇文本中提取关键信息，生成简洁的摘要，帮助用户快速了解文本的主要内容。文本摘要生成技术在自然语言处理领域具有重要的应用价值。它可以帮助用户节省时间，提高信息获取的效率。
深度学习文档 DeepLearning 0.1 documentation
weixin_34356138的博客
03-27 127
Contents LICENSE Deep Learning Tutorials Getting Started Download Datasets Notation A Primer on Supervised Optimization for Deep Learning Theano/Python Tips Classifying MNIST digits...
Convolutional Neural Networks (LeNet)--翻译
weixin_30559481的博客
08-01 182
说明： 本文是翻译了《Convolutional Neural Networks(LeNet)》 http://deeplearning.net/tutorial/lenet.html#lenet 为了督促自己学习Deep Learning和实际应用Theano，尝试翻译文章。当做笔记用，翻译的不好请见谅。 卷积神经网络 动机 卷积神经网络（CNN）是MLP的变种，受到了...
Theano-Deep Learning Tutorials 笔记:Convolutional Neural Networks (LeNet)
JiangV的博客
01-11 1972
Theano-Deep Learning Tutorials 笔记： Convolutional Neural Networks（卷积神经网络）的python实现，本节主要讲解了LeNet-5 [LeCun98]这个网络结构的实现，当时美国许多银行使用这个系统来进行手写支票的识别。
DeepLearning 0.1 documentation中文翻译_内容扉页
zhiliu的专栏
04-02 3286
本文为《DeepLearning 0.1 documentation》的中文翻译，本人水平有限，如有错误或不当，欢迎批评指正！非常感谢！原文网址：http://deeplearning.net/tutorial/contents.html注：点击英文上的链接会转到原网页，点击对应中文转到翻译网页。
Deep Learning(1)
weixin_39012047的博客
08-25 295
1.概述 Artificial Intelligence，也就是人工智能，就像长生不老和星际漫游一样，是人类最美好的梦想之一。虽然计算机技术已经取得了长足的进步，但是到目前为止，还没有一台电脑能产生“自我”的意识。是的，在人类和大量现成数据的帮助下，电脑可以表现的十分强大，但是离开了这两者，它甚至都不能分辨一个喵星人和一个汪星人。　　 深度学习隶属于机器学习，机器学习已经发展很久，在早些时候没...
第一课:Neural Networks and Deep Learning 第二周：编程作业 Logistic Regression with a Neural Network mindset
肖泽的博客
03-19 1457
第一课:Neural Networks and Deep Learning 第二周：编程作业 Logistic Regression with a Neural Network mindset Logistic Regression with a Neural Network mindset Welcome to your first (required) programming assignme...
李沐论文精读： ResNet 《Deep Residual Learning for Image Recognition》 by Kaiming He
iwill323的博客
12-14 1540
李沐论文精读： ResNet 《Deep Residual Learning for Image Recognition》 by Kaiming He
Deep Learning学习: Pytorch实现运动鞋识别
qq_42967973的博客
09-23 2003
- **🍨 本文为[🔗365天深度学习训练营](https://mp.weixin.qq.com/s/Z9yL_wt7L8aPOr9Lqb1K3w) 中的学习记录博客**>- **🍖 原作者：[K同学啊](https://mtyjkh.blog.csdn.net/)**本次实验中，掌握了在训练深度学习模型的过程中，如何使用 PyTorch 进行数据处理、模型构建、训练与测试的全流程操作。
Deep Learning, Vol. 1: From Basics to Practice
01-06
Deep Learning, Vol. 1: From Basics to Practice By 作者: Andrew Glassner Pub Date: 2018 ISBN: n/a Pages: (909 of 1750) Language: English Format: PDF People are using the tools of deep learning to change how we think about science, art, engineering, business, medicine, and even music. This book is for people who want to understand this field well enough to create deep learning systems, train them, and then use them with confidence to make their own contributions. The book takes a friendly, informal approach. Our goal is to make the ideas of this field simple and accessible to everyone, as shown in the Contents below. Since most practitioners today use one of several free, open-source deep-learning libraries to build their systems, the hard part isn’t in the programming. Rather, it’s knowing what tools to use, and when, and how. Building a working deep learning system requires making a series of technically informed choices, and with today’s tools, those choices require understanding what’s going on under the hood. This book is designed to give you that understanding. You’ll be able to choose the right kind of architecture, how to build a system that can learn, how to train it, and then how to use it to accomplish your goals. You’ll be able to read and understand the documentation for whatever library you’d like to use. And you’ll be able to follow exciting, on-going breakthroughs as they appear, because you’ll have the knowledge and vocabulary that let you read new material, and discuss it with other people doing deep learning. The book is extensively illustrated with over 1000 original figures. They are also all available for free download, for your own use. You don’t need any previous experience with machine learning or deep learning for this book. You don’t need to be a mathematician, because there’s nothing in the book harder than the occasional multiplication. You don’t need to choose a particular programming language, or library, or piece of hardware, because our approach is largely independent of those things. Our focus is on the principles and techniques that are applicable to any language, library, and hardware. Even so, practical programming is important. To stay focused, we gather our programming discussions into 3 chapters that show how to use two important and free Python libraries. Both chapters come with extensive Jupyter notebooks that contain all the code. Other chapters also offer notebooks for for every Python-generated figure. Our goal is to give you all the basics you need to understand deep learning, and then show how to use those ideas to construct your own systems. Everything is covered from the ground up, culminating in working systems illustrated with running code. The book is organized into two volumes. Volume 1 covers the basic ideas that support the field, and which form the core understanding for using these methods well. Volume 2 puts these principles into practice. Deep learning is fast becoming part of the intellectual toolkit used by scientists, artists, executives, doctors, musicians, and anyone else who wants to discover the information hiding in their data, paintings, business reports, test results, musical scores, and more.
Deep Learning Vol 1 From Basics to Practice_带书签.pdf
09-05
Deep Learning Vol 1 From Basics to Practice_带书签.pdf
Deep Learning - From basics to practice - Vol. 1
11-07
This is one of my most favorite books that teache deep learning from the very basic to advance level. The book does not emphasize on mathematics but explains the logic behind each deep learning algorithm in plain English. Even if you are the new guy in deep learning or you are already at an advance level, this book is for you. Vol 2 will be coming shortly
Deep Learning, Vol. 1 From Basics to Practice 无水印原版pdf
05-23
Deep Learning, Vol. 1 From Basics to Practice 英文无水印原版pdf pdf所有页面使用FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书
Deep Learning从入门到出世（二）
longwoo1012的博客
06-13 333
Deep Learning 圣经二. 深度网络：现代实践1. 深度学习的模型2. 深度学习的正则化3. 深度学习的优化 二. 深度网络：现代实践 这一部分是炼丹师需要掌握的核心知识，介绍了深度前馈网络，卷积网络， 循环和递归网络，实践性很强的调参技巧如深度学习的正则化，深度模型中的优化方法等等。对于长时间耕耘炼丹这一领域，是需要反复研读实践的。每个点都是可以深入坑。 1. 深度学习的模型 前馈网络 卷积网络 循环网络 递归网络 生成对抗网络 2. 深度学习的正则化 L1L^1L1和L2L^2L2范数的
用通俗易懂例子“干掉”RNN与LSTM
nobles007820的博客
05-04 833
用简单例子阐述更易理解枯燥无味的定义，本文由浅入深解析两者的工作机理，为设计程序奠定基础，主要分两部分内容： (i)举例理解循环神经网络(RNN [1], Recurrent Neural Networks) (ii)举例理解长短期记忆网络（LSTM [2] ，Long Short-Term Memory）
Deep learning：一(基础知识_1)
weixin_34336526的博客
03-14 243
  　　前言: 　　最近打算稍微系统的学习下deep learing的一些理论知识，打算采用Andrew Ng的网页教程UFLDL Tutorial，据说这个教程写得浅显易懂，也不太长。不过在这这之前还是复习下machine learning的基础知识，见网页：http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=...
深度学习（Deep Learning）——第1章 基础介绍
CodeSamer的博客
06-26 434
1、大部分人工智能任务都可以通过以下方式解决：先提取一个合适的特征集，然后将这些特征提供给简单的机器学习算法。   2、表示学习：使用机器学习来发掘表示本身，而不仅仅是把表示映射到输出；典型的例子是自编码器，由一个编码器函数和解码器函数组合而成。   3、深度学习中所谓的深度是从输入到输出的最长路径的长度。...
Deep Learning电子版
最新发布
04-29
pdfjoin -o ../DeepLearning.pdf TOC.pdf acknowledgements.pdf notation.pdf intro.pdf part_basics.pdf linear_algebra.pdf prob.pdf numerical.pdf ml.pdf part_practical.pdf mlp.pdf regularization.pdf ...
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