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Just bend down for Mummy and let Mummy do the work text me for training sessions on telegram #pegging #strapon #feminization #sissy #sub #tranning #degradation #goddesslaura #chasity #holes #mistess #slut #whiteboy #femboy #fetish #kinks

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CLIP by hand ✍️ ~ 13 steps walkthrough below CLIP, Contrastive Language-Image Pre-training, is OpenAI's answer to a question that sounds impossible: how do you put a sentence and a picture in the same space? CLIP shipped when OpenAI was still open, and those embeddings were shared far and wide. Almost every multimodal model you use today descends from them. How does it work? Goal: learn one shared embedding space for text and images. = 1. Given = A mini batch of three text-image pairs. OpenAI trained the original on 400 million. = 2. Text to vectors = Let us look up each word with word2vec. = 3. Image to vectors = We cut each image into two patches and flatten them. Now text and pixels are both just numbers. = 4. The other pairs = Repeat steps 2 and 3 for the rest of the batch. = 5. Encode = Let us push both sides through their encoders, a linear layer and a ReLU. In practice these are transformers, but the shape of the operation is the same. = 6. Mean pooling = We average across the columns, so each image and each sentence collapses to a single vector. = 7. Projection = The text vectors are 3D and the image vectors are 4D, so they cannot be compared at all. A linear layer projects both to 2D. That 2D space is the shared embedding space, and getting here is the whole point of the model. = 8. Prepare for matmul = Let us copy the text vectors down and the transposed image vectors across. = 9. MatMul = We multiply, which takes the dot product of every text vector with every image vector. Each cell is one estimate of how well a sentence matches a picture. = 10. Softmax, e to the power = Raise e to each cell. To keep it hand sized we approximate e with 3. = 11. Softmax, sum = Sum each row for image to text, each column for text to image. = 12. Softmax, normalize = Divide, and out come two similarity matrices, one per direction. = 13. Loss gradients = The targets are identity matrices: a pair that belongs together should score 1, every other cell 0. Subtract the target from the similarity and you have the gradients, in both directions. The takeaway: pairing a picture with a sentence comes down to a single dot product. Everything before step 9 is the work of getting them into one shared space, so that the dot product finally means something. 💾 Save this post!

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Americans No Longer Believe In The System 64 Year Old Woman Helped Get Rep Jamaal Bowman Elected, His Office Called For Her To Help Again. Here’s Her Response “I'm done. I'm done with politics. It's a farce. My eyes are wide open. It's a scam. These people do not work for us. They work for the special interest groups. They work for the, they definitely don't work for us when my president, the man that I voted for, can't come out and just be against burying babies alive. It's a farce. So anyway, so the kid is like, well, can we put you down? And I'm like, no. And he was like, yeah, but Jamal's a good guy. And I'm like, yeah, he's a product of a single mother. He worked in education in the Bronx. And let me tell you, I substitute to teach in the Bronx. You gotta be good. You gotta be a good person to do that because you just don't make that much money. And you gotta be a good person. I said, yeah, but give him 20 years. He'll have blood on his hands just like the rest of them because they do not work for us and they do not have any power. They have no power over things like this. So I'm done. I'm done pushing along this scam. I'm not saying this like it's gonna help because I don't think it's gonna help. I'm just not gonna do it anymore. I mean, I raised the Gen Zs, so I have some hope in the Gen Zs. They're different. I have one. They're different and these Gen Alphas, they call them the honey badgers. I mean, they're gonna have to be the honey badgers to deal with what's going on. — But me personally, the 64 year old woman sitting in that broken down old car, cause I had, you know, cause I'm broke, I'm done. —It's by design. This is on purpose and I just won't be a part of it anymore. So I tell her to the kid, and I don't know if I'm gonna get a visit from like some federal office or something, but I'm like, burn the whole MF thing down. Burn it down.”

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