@smosh_moshh: Good morning 🔥🤣 #sama28 #fyp #fypシ #everyone @Makhadzi Music

Smosh Ⓜ️oshh🧊

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Tuesday 26 May 2026 02:03:17 GMT

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Comments

karabo.moruxx :

butiiiiii ???😭😭😭🔥🔥🔥 u a divha ball

2026-06-03 09:54:43

2

H A P P I N E S S❤️ :

you have a new follower 🥳❤️❤️❤️🔥🔥

2026-05-26 13:05:13

6

Nanabae :

khavha sumbedze khldz yanga 🥰

2026-06-08 17:10:00

4

Presh🦋🥹 :

makhadzi asa mu notice tsha divha🥺🤧

2026-05-26 14:40:59

22

abigailmadi22 :

Tshinada ❤️

2026-05-27 00:02:27

2

🌹Psalms91 Girl🙏🙌🙏 :

I just followed you

2026-06-01 07:50:25

1

Sopranodx3 :

I followed back 😭📍🔥

2026-05-28 11:45:11

1

Jabusjava :

Makhadzi 💯👌

2026-05-31 02:46:53

1

Joe Santana 💯😂😂 :

You just gained a follower broo,, ishhh u kho diya👌👌

2026-05-30 02:00:32

1

MAO'S🫦Imitations :

energy on point 🥰🔥

2026-05-26 10:15:59

1

💙Lady Me❤️ :

Love your energy ❤️❤️❤️

2026-05-26 03:38:09

1

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

Other Videos

Do Men Really Make Dancing Look Like That - Or Is It Women? Responding to a video suggesting men are the reason dancing looks the way it does while women keep it modest. As someone who has been dancing for quite a few years, I break down why that take does not really hold up once you look at how things actually work. Sometimes the easiest example to reach for is the one that falls apart the fastest. #reaction #debate #logic #criticalthinking #discussion

Do Men Really Make Dancing Look Like That - Or Is It Women? Responding to a video suggesting men are the reason dancing looks the way it does while women keep it modest. As someone who has been dancing for quite a few years, I break down why that take does not really hold up once you look at how things actually work. Sometimes the easiest example to reach for is the one that falls apart the fastest. #reaction #debate #logic #criticalthinking #discussion

Geometric Measure Theory and Neural Networks Implementation in Python Understanding fundamental concepts of Geometric Measure Theory and Non-Archimedean Geometric Neural Networks through hands-on Python implementations. From Koch snowflakes to p-adic numbers, exploring practical applications in graph neural networks and molecular structure analysis. You can find, for free, this and all others slideshow on the xbe.at website. #python #computerscience #stem #mathematics #datascience #neuralnetworks #geometry #coding #programming #mathematics #machinelearning Key points for mastering Geometric Measure Theory and Neural Networks: 1. Start with fundamentals - Master basic concepts of measure theory and topology before diving into geometric aspects. Take detailed notes about definitions, theorems, and their practical implementations. 2. Build incrementally - Work through examples from simple to complex. Document your understanding of each geometric concept before moving to neural network applications. 3. Visualize everything - Create visual representations of geometric concepts. Python libraries like matplotlib and networkx are invaluable for understanding abstract mathematical concepts. 4. Test boundary conditions - When implementing algorithms, thoroughly test edge cases. Geometric measure theory often behaves unexpectedly at boundaries and singularities. 5. Connect theory to code - Always understand the mathematical foundations behind each implementation. Don't just copy code; derive the relationships between theoretical concepts and their computational representations. 6. Collaborate and share - Join mathematics and computer science communities. Share your implementations and learn from others' approaches to similar problems. 7. Practice with real data - Apply these concepts to real-world datasets. Understanding how theoretical concepts translate to practical applications is crucial for deep learning.

Geometric Measure Theory and Neural Networks Implementation in Python Understanding fundamental concepts of Geometric Measure Theory and Non-Archimedean Geometric Neural Networks through hands-on Python implementations. From Koch snowflakes to p-adic numbers, exploring practical applications in graph neural networks and molecular structure analysis. You can find, for free, this and all others slideshow on the xbe.at website. #python #computerscience #stem #mathematics #datascience #neuralnetworks #geometry #coding #programming #mathematics #machinelearning Key points for mastering Geometric Measure Theory and Neural Networks: 1. Start with fundamentals - Master basic concepts of measure theory and topology before diving into geometric aspects. Take detailed notes about definitions, theorems, and their practical implementations. 2. Build incrementally - Work through examples from simple to complex. Document your understanding of each geometric concept before moving to neural network applications. 3. Visualize everything - Create visual representations of geometric concepts. Python libraries like matplotlib and networkx are invaluable for understanding abstract mathematical concepts. 4. Test boundary conditions - When implementing algorithms, thoroughly test edge cases. Geometric measure theory often behaves unexpectedly at boundaries and singularities. 5. Connect theory to code - Always understand the mathematical foundations behind each implementation. Don't just copy code; derive the relationships between theoretical concepts and their computational representations. 6. Collaborate and share - Join mathematics and computer science communities. Share your implementations and learn from others' approaches to similar problems. 7. Practice with real data - Apply these concepts to real-world datasets. Understanding how theoretical concepts translate to practical applications is crucial for deep learning.

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