故而只因『小巧完整』，宣說 Michael Nielsen 的
Neural Networks and Deep Learning 文本︰
Neural networks are one of the most beautiful programming paradigms ever invented. In the conventional approach to programming, we tell the computer what to do, breaking big problems up into many small, precisely defined tasks that the computer can easily perform. By contrast, in a neural network we don’t tell the computer how to solve our problem. Instead, it learns from observational data, figuring out its own solution to the problem at hand.
Automatically learning from data sounds promising. However, until 2006 we didn’t know how to train neural networks to surpass more traditional approaches, except for a few specialized problems. What changed in 2006 was the discovery of techniques for learning in so-called deep neural networks. These techniques are now known as deep learning. They’ve been developed further, and today deep neural networks and deep learning achieve outstanding performance on many important problems in computer vision, speech recognition, and natural language processing. They’re being deployed on a large scale by companies such as Google, Microsoft, and Facebook.
The purpose of this book is to help you master the core concepts of neural networks, including modern techniques for deep learning. After working through the book you will have written code that uses neural networks and deep learning to solve complex pattern recognition problems. And you will have a foundation to use neural networks and deep learning to attack problems of your own devising.
……
It’s rare for a book to aim to be both principle-oriented and hands-on. But I believe you’ll learn best if we build out the fundamental ideas of neural networks. We’ll develop living code, not just abstract theory, code which you can explore and extend. This way you’ll understand the fundamentals, both in theory and practice, and be well set to add further to your knowledge.
且留大部頭之『未出版』大作
Deep Learning
An MIT Press book in preparation
Ian Goodfellow, Yoshua Bengio and Aaron Courville
The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The book will be available for sale soon, and will remain available online for free.
Citing the book in preparation
To cite this book in preparation, please use this bibtex entry:
@unpublished{Goodfellow-et-al-2016-Book,
title={Deep Learning},
author={Ian Goodfellow, Yoshua Bengio, and Aaron Courville},
note={Book in preparation for MIT Press},
url={http://www.deeplearningbook.org},
year={2016}
}
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于有興趣者自享的哩？？！！
─《W!O+ 的《小伶鼬工坊演義》︰神經網絡與深度學習【引言】》
一時為 Adam Geitgey 之簡短『人臉偵測』筆記所吸引︰
欲知其『人臉辨識』結果如何也︰
rock64@rock64:~/face_recognition/examplesffplay output.avi ffplay version 3.2.12-1~deb9u1 Copyright (c) 2003-2018 the FFmpeg developers built with gcc 6.3.0 (Debian 6.3.0-18+deb9u1) 20170516 configuration: --prefix=/usr --extra-version='1~deb9u1' --toolchain=hardened --libdir=/usr/lib/aarch64-linux-gnu --incdir=/usr/include/aarch64-linux-gnu --enable-gpl --disable-stripping --enable-avresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libebur128 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libmp3lame --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-omx --enable-openal --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libopencv --enable-libx264 --enable-shared libavutil 55. 34.101 / 55. 34.101 libavcodec 57. 64.101 / 57. 64.101 libavformat 57. 56.101 / 57. 56.101 libavdevice 57. 1.100 / 57. 1.100 libavfilter 6. 65.100 / 6. 65.100 libavresample 3. 1. 0 / 3. 1. 0 libswscale 4. 2.100 / 4. 2.100 libswresample 2. 3.100 / 2. 3.100 libpostproc 54. 1.100 / 54. 1.100 libGL error: failed to authenticate magic 1 libGL error: failed to load driver: i965 Input #0, avi, from 'output.avi': 0KB vq= 0KB sq= 0B f=0/0 Metadata: encoder : Lavf57.56.101 Duration: 00:01:18.61, start: 0.000000, bitrate: 1476 kb/s Stream #0:0: Video: mpeg4 (Simple Profile) (XVID / 0x44495658), yuv420p, 640x360 [SAR 1:1 DAR 16:9], 1471 kb/s, 29.97 fps, 29.97 tbr, 29.97 tbn, 2997 tbc 21.16 M-V: 0.134 fd= 468 aq= 0KB vq= 217KB sq= 0B f=0/0
想來『人工智慧』終現眼前呦☆
於是跟著索引︰
Articles and Guides that cover face_recognition
- My article on how Face Recognition works: Modern Face Recognition with Deep Learning
- Covers the algorithms and how they generally work
- Face recognition with OpenCV, Python, and deep learning by Adrian Rosebrock
- Covers how to use face recognition in practice
- Raspberry Pi Face Recognition by Adrian Rosebrock
- Covers how to use this on a Raspberry Pi
- Face clustering with Python by Adrian Rosebrock
- Covers how to automatically cluster photos based on who appears in each photo using unsupervised learning
How Face Recognition Works
If you want to learn how face location and recognition work instead of depending on a black box library, read my article.
Caveats
- The face recognition model is trained on adults and does not work very well on children. It tends to mix up children quite easy using the default comparison threshold of 0.6.
- Accuracy may vary between ethnic groups. Please see this wiki page for more details.
享受了趟『知性之旅』哩☺
Machine Learning is Fun!
The world’s easiest introduction to Machine Learning
Update: This article is part of a series. Check out the full series: Part 1, Part 2,Part 3, Part 4, Part 5, Part 6, Part 7 and Part 8! You can also read this article in日本語, Português, Português (alternate), Türkçe, Français, 한국어 , العَرَبِيَّة,Español (México), Español (España), Polski, Italiano, 普通话, Русский, 한국어 ,Tiếng Việt or فارسی.
Giant update: I’ve written a new book based on these articles! It not only expands and updates all my articles, but it has tons of brand new content and lots of hands-on coding projects. Check it out now!
Have you heard people talking about machine learning but only have a fuzzy idea of what that means? Are you tired of nodding your way through conversations with co-workers? Let’s change that!
This guide is for anyone who is curious about machine learning but has no idea where to start. I imagine there are a lot of people who tried reading the wikipedia article, got frustrated and gave up wishing someone would just give them a high-level explanation. That’s what this is.
The goal is be accessible to anyone — which means that there’s a lot of generalizations. But who cares? If this gets anyone more interested in ML, then mission accomplished.