wangwei
/
machinelearning_notebook
Not watched
1
0
Fork 0
Code
Releases 0 Wiki Activity
Issues 0 Pull Requests 0 Datasets Model Cloudbrain
You can not select more than 25 topics Topics must start with a chinese character,a letter or number, can include dashes ('-') and can be up to 35 characters long.
88 Commits
2 Branches
474 MB
39 Download
Tree: f9c8dadc14
Branches Tags
${ item.name }
Create branch ${ searchTerm }
from 'f9c8dadc14'
${ noResults }
machinelearning_notebook/6_pytorch/imgs/nn_lenet.png

nn_lenet.png 17 kB
Raw Normal View History

Add contents in README.md
7 years ago
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
 12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273747576777879808182838485868788 
  1. PNG

  2. 

  3. ���
IHDR��������w

  4. ���zTXtRaw profile type exif��xU
1DTmv卬}t~A	CZtOwбP<wŽ+ɮIe}hwwaC.rzܒX}~MN,ʽZ��	iTXtXML:com.adobe.xmp�����<?xpacket begin="" id="W5M0MpCehiHzreSzNTczkc9d"?>

  5. <x:xmpmeta xmlns:x="adobe:ns:meta/" x:xmptk="XMP Core 4.4.0-Exiv2">

  6.  <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">

  7.   <rdf:Description rdf:about=""

  8.     xmlns:exif="http://ns.adobe.com/exif/1.0/"

  9.     xmlns:tiff="http://ns.adobe.com/tiff/1.0/"

  10.    exif:PixelXDimension="759"

  11.    exif:PixelYDimension="209"

  12.    tiff:ImageWidth="1"

  13.    tiff:ImageHeight="209"/>

  14.  </rdf:RDF>

  15. </x:xmpmeta>

  16.                                                                                                     

  17.                                                                                                     

  18.                                                                                                     

  19.                                                                                                     

  20.                                                                                                     

  21.                                                                                                     

  22.                                                                                                     

  23.                                                                                                     

  24.                                                                                                     

  25.                                                                                                     

  26.                                                                                                     

  27.                                                                                                     

  28.                                                                                                     

  29.                                                                                                     

  30.                                                                                                     

  31.                                                                                                     

  32.                                                                                                     

  33.                                                                                                     

  34.                                                                                                     

  35.                                                                                                     

  36.                            

  37. <?xpacket end="w"?>!]D���sBITO�� �IDATxOXvA,Tp6xF`4Mo_ 
ivLZd�"&_@tbpވY]ڈ)`\QD�$H'u;WDI潤+އ{NJ!B8K@!B!B!B!B!B!PBEQՂ BCa۵,u]unv}ktYi
вn+omuA|~ +'oE!UrE)'Xtid2lh42M0EӴx|qqx<Ft:uݶ~...Mө̡˴"ʡ-O]AQz=UU5M4-c00L42m9an88N#R<qnqlYVj	WZ?}70UUGiX5<6 á+d뺦i^}]ׅYȲtVT@CH)cazA@v躮(i`0qh4N>AApqqqqqQy{REivuu5*g0)HnVAy@cUU]u4M!DDQ$+83`If+*BU!!"`t]OX2

  38. ΪO(q1 <OhH+|4
UU3L`4A$EQYyjs%nWBU!k0a0E0;ZLFq}x

  39. IUU41!LV\ʫ:B!v*'c(  k9J<LU9d!TT0xE`0L&4M9!280,BPNeٶ-;o~E˷"e{a4Yt]8(NJ8cvbU]%GV4
a4hSl}Asyטimp*Du]qn09_p8,nlEY\BrmIDAP>#EQz^EFi".6,ȵ2*f";M2 t32C C2M*BCjM +ktq

  40. &Npڒ<oHU2"BrNT.uiE8Ց=&Zn[!gW#,ؗt^%+)mA^Ƚ+vxaTЛp߂d঻l3>+xK>

  41. `ömQx,L&KLD]muPڶ=GKJ&9/..
lUz0yj{х%KU4W3糗ֱ" /Qci.+se.[;S_J#D.F++Ul[-jȥ2!cEJw~(yz2	yKv#e7S_bmFF!EQ1#˲_*Wve']W>mW9VOV%yvxZZ-vƫaJw;Cq@TNf<%E)�`hQrIOH3'aa"x$ZMY4
-3K}CF>4
ǢX8뺣(tkZ>(3Qoyz{nWδNȋ	<p'Q}2r-wSyݻw}/p#F+f�B(ו~iZ#"md2M<a,5%Wi+t܇B]]-x>1fcu}:f&EAZ

  42. 2xKp8m'xl2?N0%zK8qgÌ"4m2OKЙi8]ׅOG*I1r27""Š0fJ$z(%/QK܇BضuxٱH놴Z|9THp%7`xS%ʊdmQ~\/xabB2hRV@]b~:v]<[0qNtxAsS1ox9Tfkq(UْʟB6D(F0IT91R	u]Hi۶x:#DlzV76![r^޶v]4Ļݖx7,cbQ ub8<– p8(O2	!dy?v]9	Y.]h4Nې8N2g4yra9+֤`XK(c11pض
!r+ф3Ya~BmJ٪x,VExPXj8Pp8Dzt0#4Mx,BfG[gTc5j??3h!d-*}(

  43. Je|wGH(BP'a%mUf˗/߼y>|*˗P7f!P0k*3EQ~HkO'#S~6hل?(_z,l	M.1G;`^&eZmTfuB̲,2CF|QȂFd4Fr34OlBH~nYeY!dKh-'X^K,*nŭ69REQHk6GfaN&DrO?4JN&ͽ*DFîC!@cE

  44. EQD60
ÐW�##7AهDMȗʬL?sqx纮i((VN)�)W//xr掳~b
!T9z xG^^DI-&F(vŲ,F!a93I3p]~2207!+[M<i<#;YuSV�$j{PdDZ`0'R

  45. *"!Pux0~uqNa?_<qnFVneEd4A"Xg5p麮+Jbl�yD/xnYiL\2Ήdwk^$[k2(Yav۸DH�EZ&$뺙*2vmEǰBQNoQy7L4Mä(iWWW0O j_dn"Ze;~i‚zP(Ez4bO!2"%U&!2жm8:p8})fD9ElIzP^u^8J,GU{fսj٫EP1aqq,D3Y�sG) B}] bT-E"MJN(>0cݐ뺉>E+_&Jb"ldX$+ "~;`&4~i2WI[0	3IQaH5Ms]w4m ?F|G9Wfyf\aEjOL3SPTi4%y/p
$"DQ8QCQ̴+)M;؈<UUEtR9_mrPT~<۶̄i˟iCSiPFeqw.k 

  46. ğ@9
>6c֘9-7 u]	@XQEQNJ\XcUU"8T
: $ota|\F;V_@P:0aQ1V~֧_J@vXIuѐ'�/}w+m9dE"Sm`0HdMTr:aN	p2 fPӴd8+TSp"\5b0`0gHlXu9GeYPntu<|ky7hAXpCC

  47. *Nx?^$qɗW|VDQ0YЁ5+*"PNJ"8,a[z=?̍%!p&G
2˙pB֩d9B/I&I"0aPZ0cbe;b[JӴ^,ˊX#!Ud}3b+iRuaweEٍ?mN $JĒݻwvi
<<V>

  48. %lI:aC,g"]	u޲EXA.A8#\W0wͪ~PRk29E(,1xOQR@jΫ$kQGX+.
'zDJHOrc&$r"ȑ:Eh4b48Dq9L5@r/QؐN<b0x=E?~/jVDdnu"e],rhQFB/{1ȑ12g`p4"ap;1BdJ4L	7=S*yR8Ā뺮;NE` '1SMDrb㍭r2ΫWV2KN0tG[	ܑG@5st:(7FF+G؁IkU!,tQbyH8	7AA'N@d{^^V̺<a%`{ =$2m?
Ah׍фPlo޼9??u�_(

  49. Y2_eI`>يw`%LwQɃ>&4e"ł$guRGϋ0oJdF!`*l0e"+q9s+r5VF8Xm.0^KGZU!5"3

  50. yy"۬iǑ"eH/H53o+r:36_R	JH 2dt>#T9ԝt((~gF{ ɖiE*˗sAK˧KNEו�!),VV>AںȺ/!iiTUǃ�>tIhȗVGb"QVu]סTU]WXIE5rQ8 BfS?7u szO,Ӂ%2K”T;axE-9U!d'WUN9==C9%]I"Z&.?BCL'W):K^1X-$y:q \~Gr!.X$,03̑.y-[:U!l#K^^r
ɤYTr޽{W&烨t2[8ISm
ޚ^<8_Ų{?hJPUULXJPj/!8wy3y䉢(1֑- D!2??ݻw||<S+Xq~_ɟɳg|ߗSBH"B4)g:'?p]۱+S|W&?7|%1Msy>BCYܙY /Vi#UNS!t_L%!wm'}ݻU"T9B)tdIBd>}+)e!>}Dr!dXv]ϕ	OK(33PU"T9BSx('''X%aSO>\4%T9d_L

  51. >磏>(S,Oٺs|||B<}"W	K!qzzZób:2SP0:*%ddyJBgBPBHrjLmuӬ(	

  52. 

  53. BCȖRm]*Bs)[Rg3wsONNyzǡdT@ЖC!q:̥a{{{sĆoEӣEPlo߾(NʩZ$o޼9??ׯ_gθBBP}>ȋBYjs56dY::*Z5}Uш::*9HС!5V03w:�r!(?ܞWIWТSF(l=BH#i))th�6֖_~<x<}xx8̀BGYs||ErfѣGGGGYZZq֭roߖYݵ:PMJWiQ@BE%:~:%YZpʩI.zVP<WS?QPb6'./,8ed!$&YPݜ1:5P*CCȬρWBD^PP4X7>xϟ?O(t!:+U-wСii}#bqǏ='P4BTBgh|qb8$NZ`%ybGq!d[6:#/\a>#oͶ Fҫ	!r.Ej΄l[N:xQ½SזSJmRڿy2)[՜	-:T9K8	S&ч~*e"krWs}曢͗BB,{Ҿb%Ңߊyu34Ֆ%ӑ[2+aCβτ:H[N溪zHK"!lЙ9-:5PdTNḆ ~+Bf<* uvj5ga*Q4O|][1O !de#vN΄l\=HȠad]6Ixpqo߾]UoyI}BB**Gsq9鷢!x5o߾]ζѤ
9RaȄ
ȍ;MTn0z&iJwТ4Ɩ$CN^W4B6^ݼΐթCgsh!P4TTx:+IxJ
9B61vn\G.LQ>

  54. ϐ+jm&TBV̍օU:UN~0q||8<<\L]*nzUY

  55. ^j9888<<mJ

  56. *¿B,$ِWr///wvvr2+8}ug|55/MC6PcqnT<J*g39+ò,/lO<IL'4BCC6G9S"ZI5dqCs

  57. F:99999)Y-ΠjLiIy.D\L&,yEOڜV@$zL

  58.  B(t%t65ҋ*,|<HH0Q

  59. }[\U^[

  60. \W7

  61.  ^0B(t$tzcw1"'Ls&	 EQb/!o޼9??/1N

  62. ʙC#@0֝Dt9bJj&FyR/BB*g*'!bfjʗMfqЍ^v}yydJmv>j;7

  63. 

  64. %J

  65. K:dIpU2LAvvv(h40˷o&RuvvvEꫯ(H&?Noպ3

  66. 

  67. e<8!򱸪|6K%5\@B	_aݻt~~ņiG|~*re`{{{oݺş	)trr

  68. DOZ0QRٔs%WD^XJ$Nh7o ~('>lU)t2,

  69. %ɋPP"|e"} *=

  70. j+Drz{׿}۷omyM>c#::a*g҇
fu	C#^"?(Z-|ȌOL%8l(t(t(tr*85Q942i.o[W1s/|��<IDATW<6uvvvJ3Qh;ԯ۷opzzߧССYʩ9}oB I<R܂,4|Hi*I
mܾ};[::T9I

  71. W;МC6DYx<ONN=4}o3UۍÆЩdMiIh["I 9dH!SDYڱ%&*Hzuq	e!T9@sN+(DU?yt\NU'>_4ССY?k\@^9IVh:JA3Vy	*R|T91X9loKQ$OZЫECS3Yz$%gDB#r$t6Kqn4RF1b7*h̥aE(t(tj˷rl9::JDD!9FՕnjZ-˲,˂IYg>|ӧ=

  72. ϰ>g%–;U9DMx|XҋP^sV޿Ɲ	UΊh*CȬ|"KU<uζD

  73. RIhh!%E'OX}(Ϟ=C

  74. 	l(tT4."%&"m_aggGy萨MACwzh!)trJI'9Pҷ(Aɓ'OlWB(trr>XiիWQT>?e샀)B*gU{ｧ(۷oF۷O*(}V|S^."Afݻ;d.
ۛJC*-ntQ	+(7!dy$bq+_ٛ7ok;T<+BBg{UNF, \C;S&MJJ?90𞚹H֭[@@i&Ǐ["<"(t(tQ4pUǨ۷GGG=zy#)ϟ/\	HB,!}^zK'?Iz΍)΄W;NPC#R	6޹sիW03cY2N:+:gʩ@l'{>T?Ȏ;	#1cΝ;(ŋ`S9FX$=4L,B%JNN%}6Ӌ0_Dą~aJ|P?�zޜ1}7S{}Nˬ}yшZ7U=v^cW`JL!�<=4DKo#yPS<yxi18bTI槼Z8'''."ÌBgm՜3w'҉g-Foo~TiRMO9yx}_"hJ<z\hLN瀕srnY$wU*'̇ӧO7{^Q$o׳g~K4V烼I%s{ƴbH63astt$~oKBgV4bgMU5	KCU3xE~ggggggx~E抰/,DE<UdR!;ϼ5Vy' z'''ٹsN|.\HXh)i!i;=z1IbjΙo~|FlzW`d@Y/3]^#g_;<<L˳3({Ɠ(F4g썚lI20gqxP˴Ë҇ԙ̇LAq:ūW~*41ueƫ

  75. Я뫫_Waœ1Yʞ`5:/^x"Ϸ>?[r"?جl?~?KC6LwU7楄YHQ??\#~5+~D̷*v{
{;+TWhP嬓,H'SdABP,3sam/欣g?M}\et*QtmV?XyKCO>^˲p7T9T~agg҇

  76. 9%rB~d>w>P,.td
U!T?rq�!}4'>P:x\0*$-}�iux(t't8BV~`'!}>erJ495w_B3,eV
B|DRsɓ:

  77. tNQl?ǐ\*>}Y;ݻYV0

  78. ._įSPtVe\Dܹ2|qONN*tNHC!OD.cE2.ʬ_x!1&nUI@TceRa%
BdE󩠮Lq%dE,6CƛM&E*XPBRɓ:˓?|yC3P吙,4U?yYCb3I%'lHSR	FrPͬSp޽s�`*B6cN{||F5E|*B<$<8C"Ɩ+P9yawrsGkft&Hţ!5&jʡ!JTBC!HNJKbK@!,	qviU	6A!BL9g&QjSuq WWWWWW0=YPܾe<U!lba4kvlļDZ뺣Hul1MӶm(vEyB®}aJlX(n0DB˲8u:܈ovE!8Q/yBC!)`Ԙn0SLsaqA뺦i`8t:ضEQiӶ뺊ahY뺮 [Dün]<4MUUD4=σ6–^kIn!g8MPAղKgn/#fczb-0`NziE`0F4{y~빮kiAX5N1uaW_}5Mm<˲q(XbmF8AG|q_0pcGl۶m[4yRI4]UU}?%"q_2
RU0ı<3ZU ?躾3;IlB'0vA$!T8M_U

  79. BirPm۳zScD駟OVK.U~vo3M+F4ٌi%\Ziz^v0ȣr

  80. Sqkaa[Z!Dabt$)K4r!4xH,N漕~_W3;0~E8X!4JZ	8 <σzKlWnoi8ͲS	q4tŸB!OlqgF1;HLyGY˶z=˲Ya(.(&

  81. .E$bE}>%CYGmǻCd*’-Bj

  82. Sׁ[05k6D|߇c#&~eYhqݞNGɴu}]i뺻%x8z:hv:Mӄ!

  83. {"'x}-BEev[lB/t]7Clu4D!`A䖸% T9rXbO-iv?Ygǵ*UEQ*4CMp8dSm[r]]]S?pMlA??IF}ECL&?cf^Z/LR+fyWX,4	 2;'-y<dLJ̎`

  84. Z-YB;g1i2eJ2BCnfrB`ƪЂJ!+cT9BU!dAZs	Y3ܰwnw:˲vwwҁA '_:(
5e,BxiZNju%%,~l[vmOd2ǩɻ8u}BB7o5HKRdf:$T9k NWWW%ݯg_%DQDZ08e9# ~GYAt8NC쉍v]d4{V1'CKNDZ0Ƒ\$R=R1Jb2[62	UΪSi4!_*5V8vDz,uZ⑄-)N<j'^72lNqCA
˲D`zEWU0};

  85. в,]u]AqtEm;~/n2j(^u]={3A=bBW\`yHVk8<o$JsK5Hsh<XA4,9B򆍬/E!d\%R Wa۶1ʪaǶm#!m۽^3;-`@Qd7N2>LTU5`}GөmHWLQӴh[&t:N@nqoʶmѹm۸પ&zFD)cg`^|$c0{H)U8k>D'5HŤm9yW5s`<jh4uݶ#$sNaC,82

  86. `Veikiކu]݅_QXiv]2<o87ɧ~>zM*jߜ#,0/00m+aS8a%SʻL|jhոH(Sp0s ֬gya;Pe9b3n?~8qG&Ǫ躞D<x'I`=l3LvD'/GzmWWf50w`Kx.i11��ySa8ηWrY\|0l$HKE:VB
#$s#y8߼y8Ă#P4X �Pވb_fA?=.4=Fr\ו/xH��;#4MlgVEoiR*$>Ƒ\`@Z8DkpW??->D%'Or1z=˲vG40<

  87. lԦi0C=|&Hw~7[q1;.
PUO/heYnnz=\;N<&Xv0^LK'pvFT2W3^`@Z1eABa<LۼA+www8qG&YMZxWD/B7gf[ib.]=nN2>NUUuo;VDAo=Es)Hx>#^' y,??伣W;9V}9

  88. $+M.בI_LY',sbDׁ \n;]UU=M3\4>faPfPwyur!dP"=ey<[m@@"*B!	!BC!BC!BC!BC!BC!B!B!&�ʓ?����IENDB`

机器学习越来越多应用到飞行器、机器人等领域，其目的是利用计算机实现类似人类的智能，从而实现装备的智能化与无人化。本课程旨在引导学生掌握机器学习的基本知识、典型方法与技术，通过具体的应用案例激发学生对该学科的兴趣，鼓励学生能够从人工智能的角度来分析、解决飞行器、机器人所面临的问题和挑战。本课程主要内容包括Python编程基础，机器学习模型，无监督学习、监督学习、深度学习基础知识与实现，并学习如何利用机器学习解决实际问题，从而全面提升自我的《综合能力》。