cleanup winner README.md files
Fixed cases where there was a bad trailing backslash. Improved a few cases where a code block was needed to highlight some text. Changed a few cases where a code block needed to declared as, say, a sh code block. Fixed a few cases where the winner information was not properly formatted. Plus a few nits and clarifying text was added.
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Landon Curt Noll committed
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@@ -114,74 +114,80 @@ This will immediately start outputting gibberish to the output, e.g.
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produces:
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sins ohennAu
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T-teooclelp tiThoWy
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\
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g
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nlakuafy\
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e
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sselW usnsofueB Aoee pasfUsuslhe ooM ot Wou moy
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me neltAl -no IoyI mhuyakse inT-l chu ghenn ffo? fnsoe yhyye
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ue nnfrlass heUthole saounlcesyee pee\
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t,
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T0:0% 3.210888
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o',,vU
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An ,hTf lnm Far rur:s moilt WoEgrv wonds mith Aog thernw
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Rni So
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co Nnd :
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For an bImy pgafoun:
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Wf'r hom wortiverita
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int fod mous Eheledet,
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Tho he theket nonS wnu-ang dorlaMSp
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nrocWiSe tflg 'o.
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T0:0% 2.995950
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d whecedhencrysesil yr bn,
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we hh y thiwt
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\
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\
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hut fithlot,
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Fmdy s he alt
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Vh th no dh foud bobt werw:s Aotnf Fhwi't whe, eusu
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\
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lhh thele wewcond ary soupfy wind tDont couc ths:
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er fucwald oncli hen bos, f
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T0:1% 2.878945
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```
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sins ohennAu
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T-teooclelp tiThoWy
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g
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nlakuafy
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e
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sselW usnsofueB Aoee pasfUsuslhe ooM ot Wou moy
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me neltAl -no IoyI mhuyakse inT-l chu ghenn ffo? fnsoe yhyye
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ue nnfrlass heUthole saounlcesyee pee
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t,
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T0:0% 3.210888
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o',,vU
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An ,hTf lnm Far rur:s moilt WoEgrv wonds mith Aog thernw
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Rni So
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co Nnd :
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For an bImy pgafoun:
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Wf'r hom wortiverita
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int fod mous Eheledet,
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Tho he theket nonS wnu-ang dorlaMSp
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nrocWiSe tflg 'o.
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T0:0% 2.995950
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d whecedhencrysesil yr bn,
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we hh y thiwt
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hut fithlot,
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Fmdy s he alt
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Vh th no dh foud bobt werw:s Aotnf Fhwi't whe, eusu
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lhh thele wewcond ary soupfy wind tDont couc ths:
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er fucwald oncli hen bos, f
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T0:1% 2.878945
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```
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The gibberish is the networks attempt to write a play. So far, it's not very
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successful! Between the chunks of gibberish are training progress reports
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that look like
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T0:1% 2.878945
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```
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T0:1% 2.878945
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```
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This tells you that we are 1% through training epoch 0, and that the training
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[loss][6] is about 2.88. As the training continues, the training loss will
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reduce and the generated snippets will improve quickly:
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ses, kuth
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LAs of the wish,
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As, I nos you,
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Yov not to nalll,
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Tr tot wonds.
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\
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First Sondy, llt lrte, our, tw.
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\
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First.
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\
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BRUTUS:
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Helsting the kith gops of hoch Whay, fars surd what to,
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The cownens golt te.
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T0:12% 2.259114
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eplerrotrur tandans one wiok thy or thach and cullice ded yourssting
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And wours:
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Whed ur surt.
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\
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SINENIUS:
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On we lain bith
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rerytund: tich lon hyivetetgor.
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\
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VOLUONIA:
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He brich nom dove worthan then wise,
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T0:13% 2.254926
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```
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ses, kuth
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LAs of the wish,
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As, I nos you,
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Yov not to nalll,
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Tr tot wonds.
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First Sondy, llt lrte, our, tw.
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First.
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BRUTUS:
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Helsting the kith gops of hoch Whay, fars surd what to,
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The cownens golt te.
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T0:12% 2.259114
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eplerrotrur tandans one wiok thy or thach and cullice ded yourssting
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And wours:
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Whed ur surt.
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SINENIUS:
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On we lain bith
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rerytund: tich lon hyivetetgor.
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VOLUONIA:
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He brich nom dove worthan then wise,
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T0:13% 2.254926
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```
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It's already started to figure out things about Shakespeare plays -- how to
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spell short English words, how long lines tend to be, and that characters take
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@@ -191,40 +197,42 @@ turns speaking with their names capitalized. The training loss has dropped to
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Eventually, we will finish with the training data set and move on to a
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validation cycle:
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y fath onother,
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I sucess,
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For I me the west crare.
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\
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TRANIO:
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Whow and'd have not to had you you one in my lapteny she very ame come me a gut and shourd aghir you as ignested; shend to make I strem
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To h
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T0:99% 1.924063
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notnce gaud and is nicked thou day, ha the dusing you disaid: in thim, you things in ere thee thus erile Iht that tare theme my hast thesp thou shay: thou not eaten-or-ho-bess resing: I the but had d
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T0:99% 1.923128
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V0:0% 1.678907
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V0:9% 1.700527
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V0:18% 1.733179
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V0:27% 1.714891
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V0:36% 1.716672
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V0:45% 1.782946
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V0:54% 1.835629
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V0:63% 1.876108
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V0:72% 1.906814
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V0:81% 1.924096
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V0:90% 1.954492
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V0:99% 1.969287
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serfs you'll alliencseard:
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We
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got you? before
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I say.
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\
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Farstred dentlentecaly, sir, I it one bosticield
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All me the backnour mino,
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Whith capitaned mid! but stell the ifvemion
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Willerity.
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\
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First Cumfol of
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T1:0% 1.885619
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```
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y fath onother,
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I sucess,
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For I me the west crare.
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TRANIO:
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Whow and'd have not to had you you one in my lapteny she very ame come me a gut and shourd aghir you as ignested; shend to make I strem
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To h
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T0:99% 1.924063
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notnce gaud and is nicked thou day, ha the dusing you disaid: in thim, you things in ere thee thus erile Iht that tare theme my hast thesp thou shay: thou not eaten-or-ho-bess resing: I the but had d
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T0:99% 1.923128
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V0:0% 1.678907
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V0:9% 1.700527
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V0:18% 1.733179
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V0:27% 1.714891
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V0:36% 1.716672
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V0:45% 1.782946
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V0:54% 1.835629
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V0:63% 1.876108
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V0:72% 1.906814
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V0:81% 1.924096
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V0:90% 1.954492
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V0:99% 1.969287
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serfs you'll alliencseard:
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We
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got you? before
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I say.
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Farstred dentlentecaly, sir, I it one bosticield
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All me the backnour mino,
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Whith capitaned mid! but stell the ifvemion
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Willerity.
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First Cumfol of
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T1:0% 1.885619
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```
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Validation cycles are used to test the network to see if it has learned to
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generalize -- how well performs on data it hasn't seen before (as opposed to
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@@ -232,7 +240,9 @@ the training data that the network will see many times as it trains). Progress
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on the validation set is also displayed with a validation progress report that
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looks like
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V0:36% 1.716672
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```
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V0:36% 1.716672
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```
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which means we are 36% of the way through the validation for epoch 0, and the
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validation loss is about 1.72. Comparing the validation loss and the
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@@ -335,28 +345,30 @@ lacks history:
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produces:
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UERond w,
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\
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\
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Gir:
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KINof s, mesther s thouth.
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E:
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KINTret, at fu,
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GOMy t, as sth kesewit sooos atse ang k, ck,
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Sotheouserivesthecowhet been's, t he, h nre; t and, har wiread of pincer cedst sur has, ut:
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\
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T14:67% 2.465115
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UKESpan,
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NGaromy soreate e m esewfoure pamitherarjulthengeoly tl.
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NG s at e! w.
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\
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WAllinoully?
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Wamisw ofilem:
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I'delandinarrstath har aksubly s cath Whern t Is, weciss:
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\
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GLat s; llde.
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Y aterit dsthence\
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T14:67% 2.465404
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```
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UERond w,
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Gir:
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KINof s, mesther s thouth.
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E:
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KINTret, at fu,
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GOMy t, as sth kesewit sooos atse ang k, ck,
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Sotheouserivesthecowhet been's, t he, h nre; t and, har wiread of pincer cedst sur has, ut:
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T14:67% 2.465115
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UKESpan,
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NGaromy soreate e m esewfoure pamitherarjulthengeoly tl.
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NG s at e! w.
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WAllinoully?
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Wamisw ofilem:
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I'delandinarrstath har aksubly s cath Whern t Is, weciss:
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GLat s; llde.
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Y aterit dsthence
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T14:67% 2.465404
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```
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It is able to guess at what character is likely to follow the current one
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(by doing a [linear regression][13]), but it lacks any history beyond that to
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