@whoissamianyways66: hence prved=😭🤣 . . . . #fyp #viral #fyppppppppppppppppppppppp #new #account

whoissamianyways66
whoissamianyways66
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Region: PK
Thursday 25 June 2026 09:32:20 GMT
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malikrohan486
R~🎀 :
acha howa kabhi to L.H.S=R.H.S koi fun ma use hu nahi to mana to math ma 😂😂
2026-06-28 13:30:27
11
innocentqueentiktok0
💗Rajput زادی😜 :
good solution proud of you 😁
2026-06-28 07:01:24
2
itsnooro70
꧁༒𝓢𝔂𝓮𝓭 𝓩𝓪𝓭𝓲༒꧂ :
Amazing method😂
2026-06-30 08:11:42
3
binte__hava
Noor-e-haya^_^ :
in physics newton 3 law action reaction 😅😂
2026-06-30 18:06:26
0
user513704910
hadia :
acha tariqa he samjhane ka very good 😂😂😁
2026-06-29 18:36:27
4
zukhruf158
Fatima p4x BTS army 💞🌚👅✨💜 :
koi hal ni🙃
2026-06-30 12:24:53
1
guriya8674
guriya :
wah kia baat ha😂😂😂
2026-06-28 13:29:13
1
itx_mughal789
♛ مغل بادشاہ -🤍✨ :
Ab ye bat math me bi prove krni ha 👍🏼🤔
2026-06-25 09:41:24
5
user1970374970669
killer 💘 Queen :
right
2026-06-29 04:27:28
1
muhammadshakee321
Zåhräshakeel ❣️ :
ab ya baat math mai prove krni hai 🙄🔥😁
2026-06-28 13:33:40
1
ahmad.malik9055
Ahmad Malik :
Right 😂😂😂😂😂😂
2026-06-30 07:09:38
1
amanmughal632
princess 👑 :
Right 😂
2026-06-29 20:41:30
1
user6003600679451
ch.Umer sahi❤️‍🩹 :
bilkol
2026-06-29 17:36:37
2
its_me_jerry_10
°^🤗🥰HEER🥰🤗^° :
hehe 🤣🤣🤣
2026-06-30 01:13:45
1
emankanwal18
🎧(◕‿◕。) 𝓮𝓶𝓪𝓷(◕‿◕)♡ :
waqe 😂
2026-06-29 16:42:38
1
dua.fatima09532
@ duafatima.09 :
hmm na😂
2026-06-30 00:49:55
1
z.ki.shahdadi
Z ki shahdadi :
that's right
2026-06-30 17:18:17
0
uzairmalik520.1
Uzair Awan🔥 :
exactly 💯💯
2026-06-29 09:18:46
1
baigbaig487
[email protected] :
hhhhh 🤣
2026-06-30 06:30:09
1
zaiq05
pathani :
right
2026-06-29 17:56:04
0
manorajpoot4400
Mano-rajpoot🥰 :
right 😂
2026-06-30 20:30:47
0
mayaqueen536
😊Masoom bachi 😊 :
Bat 🦇 sai hai 😂
2026-06-30 20:36:32
0
tayyaba.khalil95
Tayyaba Khalil :
this is real
2026-06-30 10:02:37
0
mahi.malik714
🧸❀𝓜𝓪𝓱𝓲❀🧸 :
L.H.S.=R H.S
2026-06-30 06:04:58
1
sweetdraeams
✧༒☬Ⱳ𐤠ʝƖ☬༒✧ :
need GC members
2026-06-30 15:39:11
0
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Other Videos

The algorithm that put Apollo on the moon has a trust problem. 🚀
 
 Every tracking system—from a Car's autopilot to your phone’s GPS—uses a Kalman Filter to balance two conflicting sources of truth: a mathematical model (what we expect) and a noisy sensor (what we actually see). 
 
 It balances them using the
The algorithm that put Apollo on the moon has a trust problem. 🚀 Every tracking system—from a Car's autopilot to your phone’s GPS—uses a Kalman Filter to balance two conflicting sources of truth: a mathematical model (what we expect) and a noisy sensor (what we actually see). It balances them using the "Kalman Gain"—a dynamic trust dial. But classically, calculating this dial requires rigid, assumption-heavy math. If the real world gets messy, the math breaks. Enter KalmanNet. Instead of throwing away the physics to build an uninterpretable AI "black box," it pioneers a new paradigm: Model-Based Deep Learning. 🧠 **The Quick-Win Mental Model:** Think of KalmanNet as replacing the engine but keeping the chassis. • **Classical KF:** Explicit Math Memory (Covariance $P$) $\rightarrow$ Analytical Gain • **KalmanNet:** Learned Neural Memory (RNN state $s$) $\rightarrow$ Neural Gain *Rule of thumb: Keep the physics, learn the trust.* Want to see exactly how this works under the hood? In this week’s Substack Deep-Dive, we walk through the full academic proof. We explore the exact LaTeX derivations, the historical tension between Control Theory and Machine Learning, and how differentiable programming allows us to train this AI without ever providing a "true" label for the gain. 🔗 Read the full Deep-Dive at the link in our bio! 💬 **Question for the community:** Have you ever experienced the frustration of manually tuning the $Q$ and $R$ noise matrices in a classical Kalman filter? Let us know your worst tuning nightmares in the comments! 👇 #KalmanFilter #DeepLearning #ControlTheory #MachineLearning #Engineering

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