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Here we go again! ✨A new #OBJKT4OBJKT is coming! 5 years after the original one 🎂 April 7-12 Let's celebrate and discover the art from the wonderful tez community 🩶 Everyone can join! 🟨 mint 1 to 5 pieces on April 7 to 12 🟪 large edition or OE... show more
49,499 просмотров • 4 месяцев назад •via X (Twitter)
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🥳Silicone Black ICU Headgear🖤🕶️✨ Brand new micro-perforated design around the eyes — mysterious 😍yet keeps your vision clear. 📅 April 9 – April 13 (PT) | Only 5 days! 🔗 [色]Launch Price: ONLY $99 !!! 🎁 PLUS: Buy now and get a FREE Eye Mask ⚠️ Limited to just 30 pieces, act fast! And don’t miss out on the fun: ❤️ Like + 🔁 Retweet + ➕ Follow = a chance to win a full set for FREE! 🎉 We’ll select 3 lucky winners on April 12! Let’s go dark, stylish, and a little mysterious — the FeturaX way. Are you ready? #feturax #bdsm #fetish #latex #fetlife
FeturaX
25,876 просмотров • 1 год назад
![[DID YOU KNOW] Disobeying the South African Police Service National Instruction 5 of 2017 is a punishable offence in terms of disciplinary regulations. The new trend of posting pictures and video clips in uniform on various social media platforms, directly contravenes National Instruction 5 of 2017. According to Section 19(10) of National Instruction 5 of 2017 on Media Communication in the South African Police Service, the following rules must be adhered to: No member may, on their own accord, represent the SAPS on social media platforms such as Facebook, Twitter, YouTube, etc., either on their own social media accounts or those belonging to the SAPS. No member may use a profile picture of themselves in uniform or display any police insignia, badges, or logos as their avatar on their private social media accounts. Only with the approval of the Head of Corporate Communication and Liaison may a social media profile be set up for or on behalf of the SAPS on any social network. Accordingly, no station, cluster, unit, or office of the SAPS is permitted to have a social media profile or account without approval. These regulations ensure that all members execute their duties professionally within the confines of the law. Any employee who transgresses in any form or manner will be subjected to the processes of the applicable Disciplinary Regulations. All SAPS members are required to subscribe to the SAPS Code of Conduct and Dress Code and adhere to them when performing their daily duties and interacting with the public. Your efficiency, professionalism, integrity, respect, and empathy are vital to service delivery and the SAPS’s reputation, which in turn influences the community's trust in their police service.](https://image.24vids.com/tw-2045812610138841193/amplify_video_thumb/2045812511308718081/img/XYoCZE-l9LKHbtzz.jpg)
![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!](https://image.24vids.com/tw-2079570896423911664/ext_tw_video_thumb/2079570876530298880/pu/img/w_TJ6G4bD69KBKuz.jpg)
