🟩🟪🟩🟪🟩🟪🟩🟪🟩🟪🟩🟪🟩🟪 2025年12月22日、新台エヴァ導入開始！ 「e新世紀エヴァンゲリオン 〜はじまりの記憶〜」 🟩🟪🟩🟪🟩🟪🟩🟪🟩🟪🟩🟪🟩🟪 「ヤシマ作戦 第5の使徒 VS 4号機Ver.... 」が 今作で復活！show more

🟩🟩🟩🟩 🟪🟪🟪🟪 🟩🟩🟩🟩 🟪🟪🟪🟪 ～Since 2013/8/1～ いくぜマル亀！×周年月間🐢 BIG FoFo... ❣ 🐢 BIG FoFo ❣ 🐢 BIG FoFo ❣ 🐢 🟩🟩🟩🟩 🟪🟪🟪🟪 🟩🟩🟩🟩 🟪🟪🟪🟪 塩分チャージで個人的に好きなのが男梅のほし梅。 めちゃ塩辛いので、ちびちび食べるのがオススメ。 この夏はこれで乗り切ります。 🟩🟩🟩🟩明日のご案内🟪🟪🟪🟪 ➡ ８月４日（火）朝10時開店 ➡ 無職ちゃん来店予定 ➡ 賞品入荷予定 ➡ 入場抽選：入場くんオンライン∞ ⏰抽選受付：前日20:00〜当日9:30 👇マルハン亀有店LINEアカウント👇 LINEメニュー➡「登録はコチラをタップ」 ⏰番号表示：9:45〜 ⏰再整列 ：9:50〜 ⚠️店舗敷地内にお越しいただく必要がございます ⚠️ご不明な点は当店スタッフまでお気軽に ※混雑が予想されますので、お早めにお集まりください。 🟩🟩🟩🟩 🟪🟪🟪🟪 🚃最寄り駅🚃 JR亀有駅 約1km 徒歩約15分 京成お花茶屋駅 約1.4km 徒歩約20分 ※シェアサイクルステーションもございますので、自転車でのご来店も可能です。空き状況はアプリ等でご確認ください。 ※アプリ→HELLO CYCLING - シェアサイクル 🚗駐車場🚗 約400台完備 カーナビ【 葛飾区亀有4-1 】 ※満車になる場合もございます。その際は近隣の有料パーキングのご利用をお願いいたします。 ※提携パーキングはございません。show more

⬛⬜→🟥🟧🟨🟩🟦🟪

Recognise this?! Name this game. 🟩🟪🚗 #gaming #videogames #PCgames

"Current Affairs" this Monday the 22nd on Deca 🟥... 🟩 ⬛ 🟪show more

🟨🟦🟥🟩🟪🟨🟦🟥🟩🟪 昨年🔥2025年🔥に登場した 機種のNO.1をファン投票で決定 PAA主催 🏆ファンが選ぶ🏆 パチンコ・パチスロ大賞 🟨🟦🟥🟩🟪🟨🟦🟥🟩🟪 ＼＼🏆投票受付開始🏆／／ ✅何機種でも投票🆗（1メアド1投票）... ✅抽選で豪華プレゼント🎁も✨ あなたの熱き1票をお願いします！ #PAAパチンコ・パチスロ大賞show more

gm gm gm gm gm 🟥🟪🟦🟩🟨🟧 gm to 🫵... as well New York Times Square! 🟥🟩show more

BitMON has officially been acquired by Yuga Labs! 🟦🟪🟥🟧🟨🟩

How to make NFTs on Sui? We are introducing... "Joystiq" Should WeBump NFTs move to SUI? Let's VOTE! 🟧🟩 RT & Follow 🟪🟨 Joystiqshow more

🟥🟩 This is how we will invade each and... every screen in the f'ing world! We are the single most contagious virus on earth, you just can't s/top it 🟥🟩 You either join or we get to you. Open your 👀 & Expect us! 🟥🟪🟦🟩🟨🟧show more

[Deep RNN] by Hand ✍️ A Deep Recurrent Neural... Network (RNN) extends a basic single-layer RNN into multiple layers of hidden states, effectively incorporating deep learning into the RNN architecture. How does a Deep RNN work? [1] Given ↳ A sequence of four inputs X1, X2, X3, X4 ⬛️ ↳ Recurrent weights and biases for hidden layers a 🟩, b 🟧, c 🟪, and the output layer y 🟦. [2] Initialize Hidden States ↳ Set a0, b0, c0 to zeros — Process X1 (t = 1)— [3] First Hidden Layer (a) 🟩: a0 → a1 ↳ The transformation matrix is horizontal concatenation of input weights, hidden state weights and biases, visualized as [⬛️ | 🟩 | ⬜️] . ↳ The state matrix is vertical concatenation of input X1, previous hidden state a0, and an extra 1, visualized as [⬛️ ; 🟩 ; 1]. ↳ Multiply the two matrices to obtain new hidden state a1 = [0 ; 1]. [4] Second Hidden Layer (b) 🟪: b0 → b1 ↳ First layer a1 🟩 becomes the input. ↳ The transformation matrix is visualized as [🟩 | 🟪 | ⬜️]. ↳ The state matrix is the combination of a1, b0, and 1, visualized as [🟩; 🟪 ; 1]. ↳ Multiply the two matrices to obtain new hidden state b1 = [1; -1]. [5] Third Hidden Layer (c) 🟧: c0 → c1 ↳ Second layer b 🟪 becomes the input. ↳ The transformation matrix is visualized as [🟪 | 🟧 | ⬜️]. ↳ The state matrix is the combination of a1, b0, and 1, visualized as [🟪; 🟧; 1]. ↳ Multiply the two matrices to obtain new hidden state b1 = [1; -1]. [6] Output Layer (Y) 🟦 ↳ The transformation matrix is visualized as [🟧 | ⬜️]. ↳ The state matrix is the combination of c0 and , visualized as [🟧; 1]. ↳ Multiply the two matrices to obtain output Y1 = [3; 0; 3]. — Process X2 (t = 2)— [7] Previous Hidden States ↳ Copy the values of a1, b1, c1. [8] Hidden 🟩🟪🟧 + Output 🟦 ↳ Repeat [3]-[6] to obtain output Y2 = [5; 0; 4] — Process X3 (t = 3)— [9] Previous Hidden States ↳ Copy the values of a2, b2, c2. [10] Hidden 🟩🟪🟧 + Output 🟦 ↳ Repeat [3]-[6] to obtain output Y3 = [13; -1; 9] — Process X4 (t = 4)— [11] Previous Hidden States ↳ Copy the values of a3, b3, c3. [12] Hidden 🟩🟪🟧 + Output 🟦 ↳ Repeat [3]-[6] to obtain output Y4 = [15; 7; 2]show more