@lillynugnes: Still haunts me #austinbutler #elvispresley #elvis #fyp #elvismovie2022

Lilly Nugnes

Open In TikTok:

Region: US

Thursday 17 September 2026 00:06:07 GMT

6686

1737

11

81

Music

Download

No Watermark .mp4 (1.21MB) No Watermark(HD) .mp4 (0.88MB) Watermark .mp4 (1.1MB) Music .mp3

Comments

🦨adie :

HE SHOULD HAVE WON

2026-10-03 04:51:03

1

Michael Leahy :

They gotta set things right by handing it to Jafar Jackson this time around 🏆

2026-09-17 02:05:49

2

jaybyrd🙈ྀིྀི🌆🥝 :

great way to start off my morning

2026-09-30 11:16:51

4

cx_cindy.xx :

My poor baby put himself through the wringer just to bring the biggest icon to life just for him not to get that fuckin Oscar 😭

2026-09-18 19:04:56

18

asko :

you didn’t need to remind me again. I literally went to sleep thinking about this last night 💔💔

2026-09-17 22:20:52

8

☘️ :

I love brendan but austin shouldve won he would’ve dedicated his speech to lisa…

2026-09-19 21:36:27

2

natashaaa :

don't remind me. it still hurts me

2026-09-18 04:47:15

5

Farida Lewis :

yes I agree

2026-09-18 19:57:42

3

Marissa Manci :

2026-09-17 21:44:07

1

⋆ ˚｡⋆୨୧˚ :

2026-09-17 20:52:42

1

Nevia🪩 :

2026-09-21 20:37:20

1

To see more videos from user @lillynugnes, please go to the Tikwm homepage.

Other Videos

#CapCut #foryou #fry #foryoupage

#CapCut #foryou #fry #foryoupage

Máquina do tempo. | ⏱️ timebomb é real. PQP QUE FIM FOI ESSE MANO??? #EKKO #ARCANE #ZAUN #FY #EDIT #CAITVI #CLAVEDESOLY #TIMEBOMB

Máquina do tempo. | ⏱️ timebomb é real. PQP QUE FIM FOI ESSE MANO??? #EKKO #ARCANE #ZAUN #FY #EDIT #CAITVI #CLAVEDESOLY #TIMEBOMB

#pourtoiii #fyp #freestyle #danse #viralvideo mets-moi en en GIF ton moment préféré de ma danse dis-moi dans les commentaires quelles tenues choisirent pour la prochaine danse🤗🤗🤗🤗🤗🥰🥰🥰🥰🥰

#pourtoiii #fyp #freestyle #danse #viralvideo mets-moi en en GIF ton moment préféré de ma danse dis-moi dans les commentaires quelles tenues choisirent pour la prochaine danse🤗🤗🤗🤗🤗🥰🥰🥰🥰🥰

لا هاي بارحه بس المغرب اي #سليمانيه_كردستان_العراق #creatorsearchinsights #viral #yourusername #blowthisup

لا هاي بارحه بس المغرب اي #سليمانيه_كردستان_العراق #creatorsearchinsights #viral #yourusername #blowthisup

Pienso en tí #tremolo #rockespañol #rockperuano @TREMOLO21 @Elias David Fuentes

Pienso en tí #tremolo #rockespañol #rockperuano @TREMOLO21 @Elias David Fuentes

🗂 Remember these 6 algorithms if you want to enter ML ➡️Machine Learning from Scratch, part 23: 6 crucial algorithms 1. Linear regression (part 1): fits the straight line that comes closest to the points. Our 13 lines learned it step by step: error 34.92 -> 0.09 after 1,000 epochs (w 0.80, b 2.00; exact least squares: 0.80 and 2.03). Used to predict numbers, like house prices. 2. Decision tree (part 18): yes or no questions learned from 400 made-up bank customers: income above 3,020? Then: more than one missed payment? 82 of 100 new customers right, and you can read every step. 3. Random forest (part 20): 100 trees, each on its own random sample, then a majority vote. The first new customer got 15 yes and 85 no; 84 of 100 new customers right. A standard tool for table data, like fraud prediction. 4. Gradient boosting (part 22): a first guess of 47%, then 200 small trees, each adding 1% of a fix for what is still wrong: 84 of 100 new customers right. XGBoost is its best known version. 5. K-means (part 17): 600 made-up photos from three trips, no labels. Each photo joins its nearest center, each center moves to the middle of its photos. Try 5 needed 9 rounds and found the trips: city 250, coast 200, mountains 150. 6. Neural network (parts 2 and 3): one neuron draws one line; three neurons learn XOR, which no single line can separate (4 of 4 from epoch 386 on). Bigger networks read images and write text. Our small versions: part 10 reads a test digit as an 8 (90%), part 11 writes

🗂 Remember these 6 algorithms if you want to enter ML ➡️Machine Learning from Scratch, part 23: 6 crucial algorithms 1. Linear regression (part 1): fits the straight line that comes closest to the points. Our 13 lines learned it step by step: error 34.92 -> 0.09 after 1,000 epochs (w 0.80, b 2.00; exact least squares: 0.80 and 2.03). Used to predict numbers, like house prices. 2. Decision tree (part 18): yes or no questions learned from 400 made-up bank customers: income above 3,020? Then: more than one missed payment? 82 of 100 new customers right, and you can read every step. 3. Random forest (part 20): 100 trees, each on its own random sample, then a majority vote. The first new customer got 15 yes and 85 no; 84 of 100 new customers right. A standard tool for table data, like fraud prediction. 4. Gradient boosting (part 22): a first guess of 47%, then 200 small trees, each adding 1% of a fix for what is still wrong: 84 of 100 new customers right. XGBoost is its best known version. 5. K-means (part 17): 600 made-up photos from three trips, no labels. Each photo joins its nearest center, each center moves to the middle of its photos. Try 5 needed 9 rounds and found the trips: city 250, coast 200, mountains 150. 6. Neural network (parts 2 and 3): one neuron draws one line; three neurons learn XOR, which no single line can separate (4 of 4 from epoch 386 on). Bigger networks read images and write text. Our small versions: part 10 reads a test digit as an 8 (90%), part 11 writes "the bird sat on a park." #machinelearning #python #ai #datascience #algorithms

About

Robot
API

Legal

Privacy Policy