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When you let two ‘sehri babus’ sit together 😂🫶 📹 Courtesy: Samit Jadhav #RohitSharma #ShreyasIyer #SanjuSamson

89,405 Aufrufe • vor 11 Monaten •via X (Twitter)

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In my class, I teach the autoencoder by asking everyone to stand up. 🙆 Stretch your arms out wide. Imagine you are holding a heavy textbook (like Introduction to Algorithms by Prof. Cormen), the whole thing, every page. Now bring your hands slowly together until they almost touch your neck. The bottle "neck." The final exam is tomorrow and you are allowed one cheat sheet: whatever you can scribble on your palm. All nine hundred pages have to survive the squeeze. That is the encoder. Now imagine you sit in the exam. Push your arms back out to where they started. You try to rebuild the textbook from your palm notes. That is the decoder. Of course you cannot get every page back. What you get back is what mattered enough to write down, and the gap between the two is the loss the network is trying to shrink. I call it AI by Arms 🙆. It gets a laugh, and then it gets remembered. Goal: squeeze four numbers down to two, then rebuild the original four from them. 1. Given Let us start with four training examples: X1, X2, X3, X4. 2. Auto (copy to targets) We copy the training examples straight into the targets. That is the whole trick behind the name: "auto" is Greek for "self", and the data is its own label. 3. Encoder, layer 1 Let us multiply the inputs by the weights, add the biases, and apply ReLU. Negative values get crossed out and become zero. 4. Encoder, layer 2 (the bottleneck) We do it again, and now the four dimensions have become two. This layer is called the bottleneck, because everything has to fit through it. 5. Decoder, layer 1 Let us go back the other way: multiply, add, ReLU. This time there are no negatives to cross out. 6. Decoder, layer 2 We multiply once more and get the outputs Y. This is the decoder's attempt to rebuild the four original numbers from the two it was given. 7. MSE loss gradients Let us compare Y with the targets Y'. The gradient is 2 x (Y - Y'): subtract, then double. Those gradients kick off backpropagation, and the weights start to learn. Your entire education is all about encoding and decoding!

Tom Yeh

22,703 Aufrufe • vor 1 Monat

In my class, I teach the autoencoder by asking everyone to stand up. 🙆 Stretch your arms out wide. Imagine you are holding a heavy textbook (like Introduction to Algorithms by Prof. Cormen), the whole thing, every page. Now bring your hands slowly together until they almost touch your neck. The bottle "neck." The final exam is tomorrow and you are allowed one cheat sheet: whatever you can scribble on your palm. All nine hundred pages have to survive the squeeze. That is the encoder. Now imagine you sit in the exam. Push your arms back out to where they started. You try to rebuild the textbook from your palm notes. That is the decoder. Of course you cannot get every page back. What you get back is what mattered enough to write down, and the gap between the two is the loss the network is trying to shrink. I call it AI by Arms 🙆. It gets a laugh, and then it gets remembered. Goal: squeeze four numbers down to two, then rebuild the original four from them. 1. Given Let us start with four training examples: X1, X2, X3, X4. 2. Auto (copy to targets) We copy the training examples straight into the targets. That is the whole trick behind the name: "auto" is Greek for "self", and the data is its own label. 3. Encoder, layer 1 Let us multiply the inputs by the weights, add the biases, and apply ReLU. Negative values get crossed out and become zero. 4. Encoder, layer 2 (the bottleneck) We do it again, and now the four dimensions have become two. This layer is called the bottleneck, because everything has to fit through it. 5. Decoder, layer 1 Let us go back the other way: multiply, add, ReLU. This time there are no negatives to cross out. 6. Decoder, layer 2 We multiply once more and get the outputs Y. This is the decoder's attempt to rebuild the four original numbers from the two it was given. 7. MSE loss gradients Let us compare Y with the targets Y'. The gradient is 2 x (Y - Y'): subtract, then double. Those gradients kick off backpropagation, and the weights start to learn. Your entire education is all about encoding and decoding!

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10,442 Aufrufe • vor 17 Tagen

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DE GOLDSMITH🪙🪙🪙⭐ ⭐⭐

82,822 Aufrufe • vor 28 Tagen

I am living in the most shockingly miraculous season of my life. Every prayer I lift is answered the next day. I have a very sick friend I have prayed for without ceasing. In one particularly bleak moment, when I knew she needed comfort more than anything, I dropped to my knees. I asked God to let her see her father, who had already gone home, so she could feel him near. The next day she told me that both she and her nurse saw him sitting in his chair in the room. I never told her I had prayed for that. When I finally did, she was stunned. After two months in the hospital, broken into pieces, I sat buried under thousands of dollars in bills. My insurance had been cancelled over a mistake I knew nothing about. I have since fixed it. At the time, the entire weight fell on me. Those bills drained me dry. This year I have struggled more than I have in twenty years. Every time I needed just a little extra, I prayed. And every single time, something fell out of the sky in the wildest ways. This week I needed $1,000 to finally be free of every hospital bill and completely caught up. I prayed. Then the money began to arrive: payments through X (fickle at best), money from my articles, an unexpected check from Grandpa “just because,” documentary funds, and new work for radio commercials. Together it was more than I needed. For years I lived in a season of waiting. Every sign I asked for came back the same: “Trust My timing. You are in a season of patience.” The Lord held me together through every trial. I know that. But I sat in stagnation on every front. Now? Every sign points to this: “When the time is right, I the Lord will make it happen.” And it is happening. New opportunities. New love. Every debt settled. New friends I already care for deeply. Happiness. If you walk away from this post with only one thing, let it be this: If God is making you wait, there is a reason. When He decides to move, it does not trickle. It crashes over you in a wave. Roads open that you never dreamed existed. Be patient. Stay in faith. It will happen for you too.

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Vfynn_🥷🏼 𐙚

876,607 Aufrufe • vor 1 Monat

This is the account given by the woman featured in the video: A neighbor's Pit Bull kept escaping just to come sit on my porch. When I finally figured out why, I laughed before doing anything else. Not exactly the reaction my son - or even I - was expecting. For months, no one could make sense of it. He belonged to the family across the street. Young couple, two boys, the kind of household that refreshed their yard every spring and treated painting trim like a weekend project. And still... their dog kept leaving. Not to chase anything. Not to get into trouble. Not even to explore. He would cross the street, climb up my steps, and sit right at my front door like he had somewhere to be. The tirst time, I assumed he was lost. The second time, I figured their gate wasn't secure. By the third time, even his owners looked a little embarrassed — like this wasn't misbehavior anymore, it was determination. "I'm so sorry," the wife said one day, standing in my yard with an empty leash, slightly out of breath. "He got out again... and came straight here." I looked at him. Big head. White chest. Soft amber eyes beneath a face that would usually make people hesitate. There he was, sitting calmly on my porch in the quiet mountain sun, as steady as if he belonged there. When I opened the door, he didn't run. Didn't bark. He just leaned his warm body against my leg and let out a long, contented sigh. That was six years after my husband passed. Six years of living alone in Flagstaff. Six years of one cup, one plate, one empty side of the bed. People kept telling me I should get a dog, like love was something you could just pick back up when you felt ready. But I had already lost one great love. I wasn't looking for another bond that would one day leave me counting the silence again. So when that dog kept choosing my porch instead of his own home... I didn't feel chosen. I telt trapped. I just didn't know by what. It took time to understand. It wasn't pressure. It wasn't loss waiting to happen. It was something gentler. He wasn't replacing anything. He wasn't asking for anything I couldn't give. He just showed up. Again and again. Until the quiet didn't feel so heavy anymore. Eventually, his family and I stopped apologizing back and forth and started laughing about it instead. We worked out a rhythm — he'd visit, I'd keep the door open, and somehow, it all just fit. Now, he still comes by. Not because he's escaping. But because he has two homes. And for the first time in years, mine doesn't feel empty anymore.

Gabriele Corno

112,500 Aufrufe • vor 2 Monaten