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      "author": "giobardzero",
      "body": "Nice post! Definitely lots of helpful info here. I'm a piano player too, I actually recently put up a video of this very piece! Some of the stuff you talk about I also do when I practice, but then there's other stuff that I haven't tried, but makes sense and seems like a good idea. You should check out/use the #classical-music tag - the classical community here on Steemit!",
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      "body": "좋은 글들이 홍보가 안되어서 아쉽습니다. 많은 분들이 볼 수 있게 다음 글 부터는 tag에 kr 꼭 넣어주세요.^^",
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      "author": "mendylisa",
      "body": "Nice Work! Get Free Upvote here: http://thetraffic.xyz/steem/",
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      "author": "kangmo",
      "body": "VC들은 RCPS 부터 없애세요. \n\n그게 뭡니까 흐흐. LP가 그렇게 무서운가요? \n\n창업자들 목에 개목줄 하듯 계약서 쓴거에 대해 역사에 무섭진 않으신지?\n\n그렇게 원금 걱정되면 투자 말고 4년 만기 적금 드시던가요. \n\n당장 투자 못받으면 팀 해체되는 스타트업 데리고 갑질 그만하세요. 대한항공처럼 되기 전에요.\n\n자신있게 쫄지말고 투자하세요.\n\n보통주 투자할 자신도 없으면서 뭔 투잡니까 흐흐.\n\n자신있게 보통주 투자하세요.\n\n그리고 '연대보증인'으로 읽히는 '이해관계인' 투자계약서에 넣지 마세요. 쪽팔려요. \n\n자신감을 가지세요.\n\n그리고 사업 엑싯한적 없으면 창업자한테 조언하지 마세요. 뭐 알고나 하시던가요. \n\n그리고 은행들은 다른 스타트업 다 서명하는 '표준계약서'라고 들이밀지 마세요. 뻥카인거 다 알아요.\n\nICO 하는 회사들 사기꾼도 많지만 진짜도 있어요. RCPS 포기하기 싫다고 ICO 무시하지 마세요.\n\n차라리 저처럼 걍 도박한다고 하세요. 투자한다고 하지 마시구요.\n\n마지막으로 보통주 투자를 원칙으로 하는 몇 안되는 곳 여전히 있습니다. 읽는 분들 오해는 없길.",
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      "permlink": "vc",
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      "body": "Last year, I was interested in deep learning, so thought that I could apply methods and metrics from deep learning to playing the piano.\n\nI applied deep learning to practicing the piece, Chopin Nocturne Op 9-2, which was one of my favorites. Always wanted to play it but could not 'because I thought it was too hard to play' for more than twenty years.\n\nSo I devised my ways to play the piece by using deep learning approaches and learning from a few youtube videos teaching you how to practice a piece effectively.\n\nStill making many mistakes, but here is the result of my practice:\n\nThe one played at a fast tempo (with notes followed):\n\nhttps://www.youtube.com/watch?v=iTnil4z_MmI\n\nHere are some tips and tricks to practice the piano effectively and efficiently using deep learning approaches.\n\n1. Practice only one hand very slowly.\n\n1.1 Build a mixture of experts.\n\nYou need to build at least three big experts in your brain. The first is the left-hand expert, the second is the right-hand expert, and the last is the pedal expert. When you practice with two hands at the same time, your brain takes longer to train each of these two experts. Train each of these two experts separately. Also, you need to practice your pedal play separately. If you try to train any of them simultaneously in the underfitting zone, which requires more practice, it will take more time for you to train the three experts.\n\n1.2 Backpropagation takes time.\n\nThe process of adjusting weight values between your neurons, backpropagation takes time. It just takes time. So you need to give enough time for each note you play. Listening to what you play, focusing on what you see in your score all helps to adjust weight values in your neurons. But the weight values are adjusted effectively when you play slowly. Your brain needs time to store the information you perceive.\n\n2. Repeat only one measure until your hand can automatically play it.\n\n2.1 Leverage a mixture of experts.\n\nYou need to build each expert for each measure. If there are 50 measures, you are training 50 experts in your brain. When you play the piece, you will use each of these experts by gating them.\n\n2.2 Gate each expert.\n\nWhen you play the piece from the first measure to the last measure, you are gating each expert for each measure.\n\n2.3 Do curriculum learning.\n\nMaster one measure and then master another measure. In case you have 50 measures in your piece, you have 50 curriculums to learn. A measure is a curriculum. You can also make two or four consecutive measures as a group, which is another type of curriculum. Try to make a hierarchy of curriculums for your piece by grouping measures like you learn algebra, arithmetic, and geometry to master mathematics.\n\n2.4 Do transfer learning\n\nIn your piece, you may find measures with an exactly same sequence of notes. Also, there are measures just a little bit different from one in your piece. Figure out the difference and memorize the difference only. You can reduce the capacity required to play the piece by minimizing the number of experts(=the number of kinds of measures to play) you need to train.\n\n3. Practice each measure from the last measure to the first measure.\n\n3.1 Do not depend on recurrent neural network\n\nYour brain has a recurrent neural network, which tries to fetch next note based on the current note and to fetch next measure based on the current measure. But this great function becomes a hurdle when you practice the piece. Even though you do not focus on each note/measure when you play, your hand plays each of them automatically.\n\nThis is great, but playing each measure from the last to the first helps each of your experts for each measure to not depend on the recurrent neural network you have. It means, your experts will have more expertise for each measure.\n\n3.2 Provide an even opportunity for each measure to be learned\n\nWhen you play a piece you usually play from the start to the end. Usually, at the beginning, you focus on the piece but as you reach the end, you start to lose focus. To give the same amount of opportunity for each measure you practice, you also need to practice from the last measure to the first measure. That way, you start with focused at the last measure and then you slowly lose your focus when you reach the first measure.\n\nIt is important to give even 'focus' opportunities for all experts to be trained and practicing from the last measure to the first measure helps a lot.\n\n4. Do not continue to play when you made a mistake.\n\n4.1 Avoid training with examples(=scores) that have wrong labels(=wrong notes, mistakes)\n\nBasically, we are doing supervised learning. If you continue to play even though you made a mistake, your brain learns the mistake. You need to stop and play the measure from the start over and over until you do not mistake.\n\nContinuing to play the piece even though you made mistake is like playing with a score that has wrong notes. You need to stop when you made a mistake, and correct your mistake.\n\n4.2 Stop recurrent neural network to build sequences of notes with mistakes mixed.\n\nYour brain starts to build sequences of notes based on your mistake if you don't stop when you made a mistake. Before mistakes are built into the sequence of notes, you need to stop and start practicing from the start of a measure.\n\n5. Listen to different pianists playing the piece you are practicing.\n\n5.1 Leverage generative adversarial network w/ reinforcement learning.\n\nTry to listen to great pianists. Try to memorize how they play. When you practice if you make a similar sound that you heard, give positive feedbacks to you. Such as saying 'nice!', or some delicious cookie.\n\n5.2 Reduce generalization error by listening to plays from different pianists.\n\nBy listening to many players, you are not overfitting to a specific player, but learning common playing technique from many players.\n\n6. Listen/practice many times ( several hundred times ;-) )\n\n6.1 Practice as many epochs as possible.\n\nDeep learning requires training with many epochs. An epoch consists of all training examples to learn. One epoch in our case is playing the piece once. A measure is an example. Repeat as many epochs as possible.\n\n6.2 Listen to great pianists as many times as possible.\n\nYou need a great judge for your generative adversarial network. To train the judge, you need to listen to plays of great pianists a few hundred times. Focus on rubato. Different players have different styles when they play with rubato. Also, focus on articulations. Learn from them.\n\n7. When you make mistakes, just practice more. Do not give negative feedbacks to you. Making mistakes while practicing is natural.\n\n7.1 Just think your learning phase is in the underfitting zone.\n\nIn the underfitting zone, it is natural to make mistakes. The mistakes should not become negative feedbacks. If so, playing the piano will become stressful. Just take mistakes as natural.\n\n8. Memorize the piece with (1) name of each note, (2) your hand position, (3) pitch of each note, (4) chords in each measure.\n\n8.1 Use hierarchical mixture of experts\n\nWithin each expert of each measure, you can build small and many experts in it. I call these micro-experts. These small experts can output the note based on different inputs such as name/pitch of the note, chords of multiple notes, and your hand position.\n\n9. Repeat the measures you make mistakes over and over.\n\n9.1 Train a specific expert that is not trained enough.\n\nIf you make mistakes in a specific measure, it means either the expert is in the underfitting zone requiring more training, or the expert learned mistakes. You need to practice more to unlearn mistakes. You need to practice more to get out of the underfitting zone.\n\n10. Practice measures in random order.\n\n10.1 Shuffle examples(=measures) in your epoch(=piece).\n\nThis makes your experts stronger. In deep learning, when you train an epoch, it is crucial to shuffle examples in it so that the model does not have any bias on the order of examples in the epoch. Likewise, playing measures in random order helps. Just start playing any measure comes up from your mind. You can also memorize the measures you made mistake while you play, and play them in random order.\n\n11. Do not press keys on your keyboard but just touch them without making any sound.\n\n11.1 Train the hand position micro expert\n\nThis trains your specific model about the movement of your hands. By not listening to the sound at all, you can focus on the position and movement of your hands. See if your hands are prepared for each note. Your hands should be already on top of the keys you need to press, several hundred milliseconds before you press them. That way you can also control the power of touching keys to make a better sound.\n\n11.2 Train your model once more before playing in front of your friends.\n\nWhen you play in front of friends, you have a different environment. Different piano, many people around you, etc. Your brain needs time to adapt to the new environment. Unlike machines, your brain is unable to find the starting point to the trained model you when you feel nervous because of the new environment. 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