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The document appears to be a fragmented and disorganized collection of notes or instructions related to various mathematical and computational concepts, including data analysis, regression, and neural networks. It includes references to people, calculations, and technical terms, but lacks coherence and clarity. Overall, it seems to cover topics in statistics, machine learning, and data processing without a clear structure.

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46 views76 pages

1

The document appears to be a fragmented and disorganized collection of notes or instructions related to various mathematical and computational concepts, including data analysis, regression, and neural networks. It includes references to people, calculations, and technical terms, but lacks coherence and clarity. Overall, it seems to cover topics in statistics, machine learning, and data processing without a clear structure.

Uploaded by

testomkarmore
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
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FP) > Probing < ad AML: How many Layer > Activatin Funt Teo jan Options ave not geod ne data model — Train Ar Co ter t data » ced 10 clam problem 9 € § ptt AB bi images px ol Jeoo 00 224 K22H X3 iJ. Imagentt cik MMIOT: 23x28%1 DEAS [HAIL 1oKi0% erSAll —- [oP eney cafes | 14. xt 8410 laath no me Tes] 00 pee $e) Pro rn “ee ) Hla ————— | Th loz (14-5 +0 ———— 7 +4 s ee = Per s nyt \o 1 2s exrdl ‘$0 who $3e q Jom 202: ) Linear Represion Thecty & Ode 2) Poly nomial Regursion Thad & Cooke. 3) Dimensional ty Reduchon TSC uy Pe Rank ca Thc ta E*) 9 + To be Tenpei tort You Shouts Know Inept peor \ wy SO CAuaster / Stark afiarh 0.35 qe" t O node bob af ONS orn pings poctot Al from a Yond om. 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Seb_bei te L) Called. will be Not in Chil e pansat VL Child » fue. fun ae nt dla Gk ld2— wt & child ut child, Super 0)» fer snore &> Common tb cw id "name f the metrod"/ pate Clan 2 Chad \ Mode __ 2015: hepnning Method’. Dz Deke « S(mi yy fo, Gade. Cimy? Wrews Wag ~ & 9 T(r9) S Gradient dawnt dus (560) & weedy-| b |S dale o y | od: ss = Che 9 FA coh opdake we nated to Compube the Summation, fr en! Creare plas To _werolu this we take Small batch af em tes, ~t batch 6.30 2 5 b Pip, 3 pe Cd ch, > 1 de dj win have | 6 Ce ormples > how times Go nated to Loo, around the Whole daBa. ? Frwand Pre pag chor wom WH dimas hess Computation Gradient Comput ahr / a pdakion tek oy you need RA weight updates Bokeh. Whole daka b times per _updabe fm “mes per upelite So b oem S000 is a nurobe, Sha) Gs) Thus rx 100 et if] Dako MNisT. *. lo- Clow pus: 2B x 28 impuk nv obs fMLP va +BH. oalfuk redex of MEP is lo Clays; frock’ AN! MLP Relv 0 Rel = FauxksiD +512 L Jv = ginasid +sid | = sitxd +10 4o1 92.0 siz 1o 262 656 she sin T 130 ee 6 664706 input 2 bedden ° ut fag Me opt if Fined Yorroble Finad Qot \Jon_| 2.025: SVM Spent Neckot Mains Fo. Biren Claw: fication Cl -0 PB Rm clem-a Sop prt Vectors > Fy Pr Py ait af thom cu able fo Glomify “he Training Data wWithouk a exh. Mos ye > even when we hove Claus 'em balance, gum do wert 7 Hes Clas -0. Clam-4 > Sot Mangan 5 pak veckns. Clos: ty cation thw 1s a mis Clo ti Calida Ry May Ae Me Ae ca 2, % % be decision Mattila closification “ bie any C lors, y cation. oVYR Red ml Red V/s Gren Yellow, blue Gree os YetLow Gwem v/s Red, Yellow, blue Blue Yulow ig Given, Red, blue Me OVO be v]¢ Gren Red Tellow, Red fy been Gre vis Blue Avovls Yellea Red vis Blue Gran Vis Yellow Red v/s Yelow Evaluation — MeBrodds: Clomificabioa Vs Mt, » Accuracy t FF Correetty Claw i-pied examples “Total numba af Crt ammpoler.. 12. 45% ry Confusion Matrix. ' « Covrd: +ve cClaitied a4 -ve Falye -Ve * “Sam ye ave ¢ BARC: -ve Cla fred ay +ve Tet Fk _ve bal z Pet L fs am F880, ve lp L ve, © ficial Reed =e public. Tun, +ve % Actually we, C lauified ag tve True -ve * Ac tially ~e, Clanitied oy sve Fae -v¢ 4 Actually +> Clawitred a ~ Ve Fahe +ve 3 Actually VC, Clauified o1 +ve we =ve A ti al Value Pre dicted Value ln | dal | ars 2) Presi & Recalb True +ve Tus tye sp Fake -ve- y of Gore Hy labelled te 10K tom Cor, Recale = Teun We Tra me oh Fete 0 Xx of Actual +ve instore cas Preusion = Rak oro poroltted 04 pve. Achiad value Eg: Total predied oy te predicted Vols Total Actot Wwe Pictures es 3o|+ os 2 EE | _oitis doducbaladeaes 1000 Precision = _ 80 fo | ea Ot ——- - Sie s 30+ HS ao Reeal = _30 = oo mi 30 + 20 s0 3) Fi~ Sede: Combination ef, freuison & Recall C Hanmont¢ mean) Flos ae C'rsecs.c))# C} Recut) - a TPePP 4 TPR+EN TR Te | [2 te QTE RPEEN. FR Re above — ertampl: 60 | | one. Flos 2 30 i 24 30dhe+20 126 Peciadt value foe ve) | “Ve t Ace = OFM. 4u0 |S40 | ho Pred preusion+ 390 = O-1T 390+H0 Re Galt - 890 = 0:45 290420 -So = Tet 248% = 0546 2890 +40 +20 237 Jon 2ors: Ki repro, | Le beg tenn! Clartering “ Groves, K- groups Chy pe prameler meoms Average We grow te aka into vk! geups bacD on the heganew to Pe mean of tas grops. Steps: ) Intiohge the Chute. Cembroiols . 2) Atslgn point clus tern 38> Updale — Centos dr . uw) Y eak - ni Keo ot) * 0 (20 ney (2 (2.2) (as) Gisd (sd (C7) G+ | eno) 62+ bss) Romolo m Inartiohiz ahion how find the Similarty Cclosenen } ae Ke, - 1) * om (4-995 v bad + eH) dist & Ct (01) Eudhileom — obet- dist to 6, 7 Sy), tol ° Views +t = Ven ad Vu-o dD" = 4 VO ba|. ¢ an 4 Year is = = G2) e v Qu +22)" = JE Yasnttos = ve (95) 7 Vat ist evan o ec) ~ Jleot tea . Jar Vex r69* = 1 “ (66) Lev Atay = ai 3 i (0 Vr Vz Clutert 6 , if r Chute 2 du ta (ss) Cx.) || p Ces) (ry 1 uv Ss.) (2.2) nu 6. 6) mime (6/4 Yu) - US 4 Gs,| £5) Repeak The whole pros FM he Stobi bed. cl Ce ¢ (uw (aw js) uo el Oo Ve z eo ce a 0 9 Gon 45) Cuen Cortro AL th et yy err ahy + [ee b= (2 = Ye H (Se st J Fee 201s Mhuanpon | > Wher of MPI FC is kroun joPIE | page 8 nat te Riddim Layes. > Foe at produ prpose Om Imoge sla matrix. >| Usa MiP pA Images clicthy Hae Spatial info Comet 1b to A Aleck, F Conve lution ©VPenstian. (Cutt Velame (2) Aerts) wa stet) togge movewent iter W Gxe3) ait Fier Wo (323) nut Volume (+pad 1) 02783) 2 oo 00000 Bue padding - 4 2 sree 2 A Filten We Se = 3 ng & How Com pul the Output Size, Comider te s]p ‘pefohe padding : Haw Dy! Sx 5x3 wd filter: kh ~2 Filta) lege F |: Stride S 2 Padding i 4 BZ x9 xr thom, the o}m will be Uy * WW, *D, Hy (i ete 44 § -3 4241 4) $s eee L Wo = (we - F +203) 41 Dd = * Thane em |__ image, 28x26 «3 we need Convert “Riis inte —__om_|_block _of DX Dk X16 hat | ghoul be Ake fare octens of cis Conyaluction Raye k =|? 2€ ~218 x3 2h A DU Khe Z|~e Hy x wexD; >| Ho Kino * Do Sle 2 t R-? =3 6 pip [Elsen tel il | (ep a ee Ca? See t We Fro) et . (=) ) Ee 2H “(PE sEe*)t > © sg 2H ={] 2eefitah | fl [4 fs Z 7 plelo I 23 = Soe lea +f et 5 . ag 1 Sra hs Solua 4 Ib Sel © A iC l QO. G 7 4 ¢ + a i — a a rt “| 4a } “ Bs “ w < \ 1 : g Ae «88 8 3 4 “4 i CL pe zs 7 =p a z “ aaa i “I N a . auus A t Com biai murlip Conv om FKP Ss q y i cowl 4 3 Oo | cow2 33 9 4 2 == 3x8x3 Hoe 82M 44 Hye 6-3 4240 rin } L aS =e <3 He CS) 4 s il ie Ps { ! 24 x5 {ep Feat. E pthcter alae wt ty Decists head con a Clamex — 5 ( ~2Z«s) +S (zxysy) + 3 | (34G) +3) ) t ed # fits Sing pie S| t 30 40 = 22) Avg pooling Mon pooling. Kerab Se Strike wetpsivn nae aun | [34 a Single depth slice pool 44 2i4 6i7 8 | I 3/2 [ilo] 1[2 [3] 4) a 12 2 y Pctng layer downeampies the volume stat, independent in each depth sie ofthe input vlurne. Left nthis example the Input volare of size [224322464 is pooled with iter size 2, sve 2 into output volume of size [11271 2x64, Notice thatthe volume depth is preserved. Right: The most common dossarpling operation's max, gg rise to mex pooling exe shown ‘tha etice of? Thatle each mane taken over A murere (iia 22 equate)

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