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🚀 Unich Network Ambassador Program: Cycle Update! Our weekly training cycles will run from Tuesday to Tuesday - but we are making a special update for our very first Batch! To give everyone enough time to apply and get fully comfortable with the program's workflow, Batch 1 will be...

36,906 Aufrufe • vor 3 Monaten •via X (Twitter)

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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

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Tuesday, April 14, 2026 at 6:52 AM: What you have witnessed yesterday and this past weekend across California and parts of Southern California is the birth pains of an El Niño development and the ongoing escalation the warming of the Pacific torrential rains came through the San Fernando Valley with such strong, thunderstorms that it caused significant flooding in the Sherman Oaks, Universal City area around 1:05 PM - 1:35 PM yesterday on April 13, 2026. This powerful storm packed winds gusts of over 50 mph and it’s a true testament that we are no longer in a pattern that brings rain in just winter time but is now extending into April and May and beyond with that said we have another storm on the heels that will be arriving very quickly on April 21 - 27, 2026. The next storm will also bring more heavy rain, thunder, and lightning, and yes, more snowfall to the local mountains. As the Pacific continues to warm, the weather will become more drastic with extreme heat waves and more unusual severe rain events for California. This situation will get quite scary if it continues to verify by the end of October and November with the super El Niño development and the atmosphere connecting something that we’ve not seen since 1982, 1997/ 1998. The main Marshall Islands low axis is already gearing up for this major event this year which you’ve seen an example already. The flooding in the Hawaiian islands. Typhoon Sinlaku has now hit the Guam region with a category five status this early in April all due to the warming of the Pacific and the emergence of El Niño. #CAwx

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