@kheylae_fastlife: Toujours sur son tel 😳

Kheylae_FastLife

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Thursday 08 January 2026 09:55:37 GMT

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Athena :

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2026-01-08 16:38:21

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#somalitiktok #SHUUTE #bokool ☠️☠️☠️☠️☠️☠️☠️☠️☠️☠️☠️☠️☠️☠️☠️☠️☠️☠️☠️☠️☠️

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Hi you guys! Let’s steam some cow custard buns together :)

Hi you guys! Let’s steam some cow custard buns together :)

➡️ Machine Learning from Scratch, part 31: the transformer. A transformer turns every token into a list of numbers, adds its position, and then lets every token look at the others. That step is called attention: each token has a query (what am I looking for), a key (what do I contain) and a value (what I pass on). The better a query matches a key, the more of that value flows in. Real transformers repeat this in many layers with several attention heads each. The design comes from the 2017 Google paper

➡️ Machine Learning from Scratch, part 31: the transformer. A transformer turns every token into a list of numbers, adds its position, and then lets every token look at the others. That step is called attention: each token has a query (what am I looking for), a key (what do I contain) and a value (what I pass on). The better a query matches a key, the more of that value flows in. Real transformers repeat this in many layers with several attention heads each. The design comes from the 2017 Google paper "Attention Is All You Need" by Ashish Vaswani and colleagues, first used for translation. A large language model is a transformer trained to predict the next token, again and again, on a huge amount of text. Our own run, nothing downloaded. attention.py is one self-attention layer in 16 lines of numpy. Every word gets a query, a key and a value of 8 numbers, starting random, trained from scratch with hand-written backpropagation on 192 made-up sentences like "the dog ate the carrot because it was hungry". The task: find out which pet "it" means. On 64 new test sentences "it" puts 99.95 percent of its attention on the right word on average (the lowest single sentence gets 99.9 percent; the program prints this as 100%). Before training it was 10 percent, spread evenly. The cat sentence from the video is one of the test sentences. Honest notes: our task is easy on purpose. Every sentence has exactly one pet, so "it" only has to learn to look for pets, and it learns that within the first pass. Real sentences with two candidates need more context, which is what many layers are for. Our small version has no positions, no scaling, no mask, one head, and only "it" asks a question. Every word of the test sentences also appears in training; the combinations are new. The 3D space in the video shows the real keys of our model, projected to three axes (two of them stretched so you can see them). The stacked layers are a sketch. #machinelearning #ai #transformer #python #llm

#لايك_متابعه_اكسبلور

#لايك_متابعه_اكسبلور

Thanks @creditkarmafor sponsoring the vid 🙏#TopOfYourMoney

Thanks @creditkarmafor sponsoring the vid 🙏#TopOfYourMoney

#creatorsearchinsights #سورة مريم كامله للقارئ: سعود الفايز #سعود_الفايز#سورة_مريم #راحه_نفسيه #القران_الكريم#quran ##creatorsearchinsights #القرآن_الكريم #ارح_سمعك_بالقران #القرآن_الكريم_راحه_سمعك_القرآن💙🎧 #القارئ_محمد_الفقيه #محمد_الفقيه #الشيخ_محمد_الفقيه #karimwce16p #تلاوة_خاشعة #سوره_الكهف

#creatorsearchinsights #سورة مريم كامله للقارئ: سعود الفايز #سعود_الفايز#سورة_مريم #راحه_نفسيه #القران_الكريم#quran ##creatorsearchinsights #القرآن_الكريم #ارح_سمعك_بالقران #القرآن_الكريم_راحه_سمعك_القرآن💙🎧 #القارئ_محمد_الفقيه #محمد_الفقيه #الشيخ_محمد_الفقيه #karimwce16p #تلاوة_خاشعة #سوره_الكهف

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