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@gwxxweps: #рекомендации #fyp #актив #хочуврек #пжврек

𝒷ℴ𝓃𝓎𝒶..
𝒷ℴ𝓃𝓎𝒶..
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Region: UA
Tuesday 30 June 2026 03:41:27 GMT
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Comments

kazanov1990
...🎧 :
ой а как вы догадались...
2026-06-30 18:26:22
18
.eva0236
✪【о】【с】【д】✪ :
я теперь хочу вкусняшки
2026-06-30 13:31:37
6
milkyway_554
Дэнчик :
Наша группа в рандомный момент:
2026-06-30 20:31:07
0
stroitel_potter
#строитель#гарри_поттер :
боты+я=
2026-06-30 19:29:17
0
hijdjdhgc
... :
было дело
2026-06-30 04:29:41
1
user328766800191
кареглазая🫀🤟 :
название песни
2026-06-30 19:56:14
0
nino5639382939109777
без частей :
ой
2026-06-30 18:46:53
0
seahouse67
💨ⲇυⲁⲏⲁⳝυⲥⲁⲕⲟⲇυⲗ💨 :
кст ты в реках
2026-06-30 14:34:32
0
ranter_compot
💙Компот рантер❤️ :
@Бледный гость.
2026-06-30 18:51:30
0
zhordextop4ik
Zhordex :
😂😂😂
2026-06-30 03:44:52
1
v_e_r_y333
_varya_4ka 🇨🇷 :
@mari🪷 на нч
2026-06-30 16:17:47
0
organsmzkr6
Виолетта🎀 :
@KIRIll
2026-06-30 19:02:54
0
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How does an AI actually "see" a video game? 🎮🤖 Most people think a neural network acts like a remote control, outputting a command like "Go Right," or a probability like "80% chance to go right." This is fundamentally wrong. In Deep Q-Learning, the AI acts like a mathematical real estate appraiser. It doesn't make decisions; it assigns a "Future Profitability Score" (a Q-value) to every possible timeline. 🧠 **The Quick-Win Mental Model: The P.A.C. Loop** To understand Reinforcement Learning, just remember **Predict, Act, Correct**: 1️⃣ **Predict:** The network looks at the state vector and puts a price tag on every possible move. 2️⃣ **Act:** It usually picks the highest price tag, but 10% of the time it forces itself to do something totally random to discover hidden shortcuts ($\epsilon$-greedy). 3️⃣ **Correct:** It takes a step, gets a reward, and uses the famous Bellman Equation to mathematically correct its past prediction using its new reality. It literally bootstraps its own intelligence. 🔗 **Want the full mathematical proof?** If you want to see the exact calculus, the gradient descent update step, and the Python logic behind this, I’ve written a full academic Deep-Dive on Substack. Hit the link in my bio to read it! 👇 **Question for you:** Did you realize that reinforcement learning networks output expected values (which can be negative!) instead of probabilities, or is this a completely new mental model for you? Let me know in the comments! #ReinforcementLearning #DeepLearning #MachineLearning #ArtificialIntelligence #MathNotes
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