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👑MODEL'S ENGAGEMENT THREAD👑 👣Theme: Foot Fetish Friday - Any feet content!👣 Featured 👑 AnasFeet 👈🏻FOLLOW! 👉🏻Repost to participate, I will retweet back the best entries and pick 1 or 2 participating model/s to FEATURE next thread! 🦄

12,317 görüntüleme • 2 yıl önce •via X (Twitter)

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$FEET🦶 by Feet Pix 🦶📸 weekly update: Airdrop for NFT holders coming soon 🦶 New CEX listing coming this week 🛒 Halving for stakers coming in 7 days 🥩 FEET is the perfect mix of $PEPE, $ETH, and $BTC 🧠 Highlights 🔦 Last Friday, the FP of #feetpixwtf doubled up from 0.05 to 0.12 📈, the best performance of the week for an NFT collection 🏆 The team is keeping its promise of using the creator fees to buy back and burn the NFTs, and we are approaching our 1:1 airdrop! 🎁 It will be unlocked once the supply goes down to 8,300; we are 87 NFTs away! It's impressive—it was 10,000 on February 14th! 🤯 The community is expanding, with Chinese influencers and worldwide celebrities tweeting about $FEET 🦶🌍 The Twitter account is about to reach 20K followers! 🫂 I think there will be many chances for feet to become mainstream during the summer (everyone takes pics of themselves on a beautiful beach) 👙⛱️ The price is stable with over 1M market cap, so we have great chances to go parabolic once the volume kicks in! 🎢 The team has no team tokens, and the ownership is renounced, so there will be no selling pressure from the devs.🦄 It's just a matter of time! The rise, as I've always said, is #inevitable!! 🚀 Feet fam, let’s make some noise! 💥 Like, RT this long tweet, and for those who FOLLOW me and read the tweet until here, I will do a giveaway of 50M $FEET to a lucky feet fan ❤️🦶🍀 #FEET #CRYPTO

Belen Franchese

166,097 görüntüleme • 3 yıl önce

Batch Normalization by hand ✍️ ~ 7 steps walkthrough below Batch normalization is common practice for improving training and achieving faster convergence. It sounds simple. But it is often misunderstood. 🤔 Does batch normalization involve trainable parameters, tunable hyper-parameters, or both? 🤔 Is batch normalization applied to inputs, features, weights, biases, or outputs? 🤔 How is batch normalization different from layer normalization? So I drew and calculated one entirely by hand. Goal: normalize a mini-batch of 4 examples to mean 0 and variance 1, then let the network scale it back. = 1. Given = A mini-batch of 4 training examples, each with 3 features. = 2. Linear layer = Let us multiply by the weights and add the biases. Batch norm sits after this, which answers the second question: what gets normalized is features, not inputs, weights or biases. = 3. ReLU = We apply the activation, and -2 becomes 0. Negative values are suppressed before any statistic is taken. = 4. Batch statistics = Let us compute the sum, mean, variance and standard deviation, one row at a time. A row is a feature and the four columns are the four examples, so every number here measures one feature against the rest of the batch. That is the "batch" in batch normalization, and it is exactly what layer normalization does not do. The statistics are rounded to whole numbers, which is what keeps the rest of the page doable in pen. = 5. Shift to mean 0 = We subtract the mean, in green. The four values in each feature now average to zero. = 6. Scale to variance 1 = Let us divide by the standard deviation, in orange. Each feature now has variance one, whatever scale it arrived at. = 7. Scale and shift = We multiply by a linear transformation and pass the result on. The diagonal and the last column are trainable, so having just forced every feature to mean 0 and variance 1, we hand the network the means to undo it. The outputs: Mean of each feature = [2, 1, 2] Std dev of each feature = [1, 1, 2] To the next layer = [2, -2, 2, 0], [-3, 3, 6, -3], [2, 0, 1, 2] The answers: 🤔 Both. The scale and shift are trainable, the statistics are not. Epsilon and the momentum on the running statistics are the hyper-parameters, and one mini-batch by hand needs neither. 🤔 Features, after the linear layer, not inputs, weights or biases. 🤔 Batch norm measures across the batch, one feature at a time. Layer norm measures across the features, one example at a time. 💾 Save this post!

Tom Yeh

20,616 görüntüleme • 12 gün önce