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실습1_반복&복제
실습2_흐름블록
실습3_복제게임
실습4_상어피하기
실습5_치즈먹기

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실습1_ 야구게임

실습2,3_ 도장찍기(붓/연필)
실습4_ 축구
실습5_ 방향키
실습6_ 흐름반복

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- 항상 노트에 '문제', '문제분석', '절차'를 먼저 작성하고 프로그램 만들기

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- 조편성할때 인원수를 어떻게 조정할 것인지 대비할 것
- PPT화면이 아니라 앞을 보고 수업진행할 것
- 인터넷안될상황을 대비하여 오프라인수업도 준비할 것
- 스크래치보다는 앤트리가 주류

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ppt파일로 내보내기한건데 링크안됨미다

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ppt파일로 내보내기한건데 링크안됨미다

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발표할땐 링크로 써야해요!!!!!

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5조(프로젝트 발표자료)

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1. 내가 생각하는 AI는 무엇인가요?
2.오늘 실습한 것 중 가장 재미 있었던 활동은?
3.AI  활용한 그림 or 음악 활동 후 느낀 점은?
4.AI를 적용하고 싶은 부분은?

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오른쪽 하단의 +를 눌러서 슬라이드쇼를 클릭해주세요

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위 이미지는 무료 디자인툴 미리캔버스에서 만들었어요.

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draw together with a recurrent neural network model

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https://www.miricanvas.com/design
카드뉴스

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Even if you are not a design expert, you can easily create the design you want with free templates.

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Semi-Conductor is an experiment that lets you conduct your own orchestra through your browser. You can move your arms to change the tempo, volume, and instrumentation of a piece of music.

It uses Tensorflow.js, a machine learning library that works in the browser, to map out your movements through your webcam. An algorithm plays along to the score as you conduct, using hundreds of tiny audio files from live recorded instruments.

Find more information about PoseNet on the TensorFlow blog.

Built by Rupert Parry, Melissa Lu, Haylie Craig and Samantha Cordingley from Google Creative Lab, Sydney.

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An AI experiment to conduct an orchestra inside your web browser.

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A game based on a musical machine learning algorithm which can generate melodies. The player has to finish the side-scrolling game to listen to the full song.

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Semantris is a word association game powered by machine learning.

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The name of the service has changed Thank you for your patronage with PaintsChainer. Three years after its launch, we decided to change the name of the service to "Petalica Paint". We like to think of the creations blooming on this service as multicolored, like the petals of a flower, hence the new name's motif.

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이미지에서 배경을 제거하세요 100 % 자동 및 무료

클릭 한 번으로 5초 만에 100% 자동 배경 제거

사진 배경을 수동으로 지우는 것보다 이 세상에는 재미있는 일이 수천 수만가지 있습니다.

Remove.bg의 스마트 AI로 사진 편집 시간은 줄이고 더 흥미로운 일을 시작해보세요!

다양한 소프트웨어와 통합됩니다.

가장 인기 있는 디자인 프로그램, 인터넷 쇼핑몰, 컴퓨터 환경에서 이용 가능한 버전과 플러그인을 제공해드립니다. 지금 Remove.bg의 API를 사용하시고 이미지 편집 업무의 효율을 더욱 높여보세요!

모든 플러그인과 다운로드 항목 API 문서
창의성과 효율성을 높이세요!

친구를 깜짝 놀라게 해 줄 멋진 편지를 만들고 싶을 때나 웹사이트에 업로드할 자동차 사진 수천 장을 편집해야 할 때 remove.bg를 이용해보세요.

{:class=>"text-typo w-9 h-9"}매직 브러쉬 {:class=>"text-typo w-9 h-9"}개인용 {:class=>"text-typo w-9 h-9"}포토그래퍼용 마케팅용 {:class=>"text-typo w-9 h-9"}개발자용 {:class=>"text-typo w-9 h-9"}전자상거래용 {:class=>"text-typo w-9 h-9"}미디어용 {:class=>"text-typo w-9 h-9"}자동차 대리점용 {:class=>"text-typo w-9 h-9"}기업용

Nov 23, 2023
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Partial Background Removal: Get Rid of People, Products, & Objects in Photos

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사진이나 이미지 배경을 한 번 클릭으로 5초만에 무료로 제거할 수 있습니다. 이미지 배경 투명하게 만드는 법. 누끼 따기 프로그램.

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How were these built?

All our experiments are all built with freely accessible web technology such as Web Audio API, WebMIDI, Tone.js, and more. These tools make it easier for coders to build new interactive music experiences. You can get the open-source code to lots of these experiments here on Github.

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Music is for everyone. Play with simple experiments that let anyone, of any age, explore how music works.

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This experiment lets you play with new sounds created with machine learning. It's built using Nsynth, a research project that trained a neural network on over 300,000 instrument sounds. NSynth is able to combine sounds, like a bass and flute, into a new, hybrid bass-flute sound. This experiment lets anyone explore these sounds and make music with them.

Built by the Magenta and Creative Lab teams at Google. NSynth is built using Tensorflow, Tone.js and a Wavenet-style autoencoder. Learn about NSynth here.

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NSynth: Sound Maker by Yotam Mann - Experiments with Google

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Since 2009, coders have created thousands of amazing experiments using Chrome, Android, AI, WebVR, AR and more. We're showcasing projects here, along with helpful tools and resources, to inspire others to create new experiments.

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A fun way to explore music using machine learning.
Just pull the blocks apart to see what melodies you discover.

Built with TensorFlow.js + p5.js + MusicVAE.js.
Learn more about how it works.

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A fun way to explore melodies using machine learning.

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Google Arts & Culture features content from over 2000 leading museums and archives who have partnered with the Google Cultural Institute to bring the world's treasures online.

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We've created MuseNet, a deep neural network that can generate 4-minute musical compositions with 10 different instruments, and can combine styles from country to Mozart to the Beatles. MuseNet was not explicitly programmed with our understanding of music, but instead discovered patterns of harmony, rhythm, and style by learning to predict the next token in hundreds of thousands of MIDI files. MuseNet uses the same general-purpose unsupervised technology as GPT-2, a large-scale transformer model trained to predict the next token in a sequence, whether audio or text.

Samples

Since MuseNet knows many different styles, we can blend generations in novel ways. Here the model is given the first 6 notes of a Chopin Nocturne, but is asked to generate a piece in a pop style with piano, drums, bass, and guitar. The model manages to blend the two styles convincingly, with the full band joining in at around the 30 second mark:

Try MuseNet

We're excited to see how musicians and non-musicians alike will use MuseNet to create new compositions!

In simple mode (shown by default), you'll hear random uncurated samples that we've pre-generated. Choose a composer or style, an optional start of a famous piece, and start generating. This lets you explore the variety of musical styles the model can create. In advanced mode you can interact with the model directly. The completions will take longer, but you'll be creating an entirely new piece.
Note: advanced mode of the interactive demo is currently offline.

Some of MuseNet's limitations include:

The instruments you ask for are strong suggestions, not requirements. MuseNet generates each note by calculating the probabilities across all possible notes and instruments. The model shifts to make your instrument choices more likely, but there's always a chance it will choose something else.

MuseNet has a more difficult time with odd pairings of styles and instruments (such as Chopin with bass and drums). Generations will be more natural if you pick instruments closest to the composer or band's usual style.

Composer and Instrumentation Tokens

We created composer and instrumentation tokens to give more control over the kinds of samples MuseNet generates. During training time, these composer and instrumentation tokens were prepended to each sample, so the model would learn to use this information in making note predictions. At generation time, we can then condition the model to create samples in a chosen style by starting with a prompt such as a Rachmaninoff piano start:

Or prompted with the band Journey, with piano, bass, guitar, and drums:

We can visualize the embeddings from MuseNet to gain insight into what the model has learned. Here we use t-SNE to create a 2-D map of the cosine similarity of various musical composer and style embeddings.

Long-Term Structure

MuseNet uses the recompute and optimized kernels of Sparse Transformer to train a 72-layer network with 24 attention heads-with full attention over a context of 4096 tokens. This long context may be one reason why it is able to remember long-term structure in a piece, like in the following sample imitating Chopin:

It can also create musical melodic structures, as in this sample imitating Mozart:

Music generation is a useful domain for testing the Sparse Transformer as it sits on a middle ground between text and images. It has the fluid token structure of text (in images you can look back N tokens and find the row above, whereas in music there's not a fixed number for looking back to the previous measure). Yet we can easily hear whether the model is capturing long term structure on the order of hundreds to thousands of tokens. It's much more obvious if a music model messes up structure by changing the rhythm, in a way that it's less clear if a text model goes on a brief tangent.

Dataset

We collected training data for MuseNet from many different sources. ClassicalArchives and BitMidi donated their large collections of MIDI files for this project, and we also found several collections online, including jazz, pop, African, Indian, and Arabic styles. Additionally, we used the MAESTRO dataset.

The transformer is trained on sequential data: given a set of notes, we ask it to predict the upcoming note. We experimented with several different ways to encode the MIDI files into tokens suitable for this task. First, a chordwise approach that considered every combination of notes sounding at one time as an individual "chord", and assigned a token to each chord. Second, we tried condensing the musical patterns by only focusing on the starts of notes, and tried further compressing that using a byte pair encoding scheme.

We also tried two different methods of marking the passage of time: either tokens that were scaled according to the piece's tempo (so that the tokens represented a musical beat or fraction of a beat), or tokens that marked absolute time in seconds. We landed on an encoding that combines expressivity with conciseness: combining the pitch, volume, and instrument information into a single token.

bach piano_strings start tempo90 piano:v72:G1 piano:v72:G2 piano:v72:B4 piano:v72:D4 violin:v80:G4 piano:v72:G4 piano:v72:B5 piano:v72:D5 wait:12 piano:v0:B5 wait:5 piano:v72:D5 wait:12 piano:v0:D5 wait:4 piano:v0:G1 piano:v0:G2 piano:v0:B4 piano:v0:D4 violin:v0:G4 piano:v0:G4 wait:1 piano:v72:G5 wait:12 piano:v0:G5 wait:5 piano:v72:D5 wait:12 piano:v0:D5 wait:5 piano:v72:B5 wait:12

During training, we:

1. Transpose the notes by raising and lowering the pitches (later in training, we reduce the amount of transposition so that generations stay within the individual instrument ranges).

2. Augment the volumes, turning up or turning down the overall volumes of the various samples.

3. Augment timing (when using the absolute time in seconds encoding), effectively slightly slowing or speeding up the pieces.

4. Use mixup on the token embedding space

We also create an inner critic: the model is asked during training time to predict whether a given sample is truly from the dataset or if it is one of the model's own past generations. This score is used to select samples at generation time.

Embeddings

We added several different kinds of embeddings to give the model more structural context. In addition to the standard positional embeddings, we added a learned embedding that tracks the passage of time in a given sample. This way, all of the notes that sound at the same time are given the same timing embedding. We then add an embedding for each note in a chord (this mimics relative attention, since it will be easier for the model to learn that note 4 needs to look back at note 3, or else at note 4 of the previous chord). Finally, we add two structural embeddings which tell the model where a given musical sample is within the larger musical piece. One embedding divides the larger piece into 128 parts, while the second encoding is a countdown from 127 to 0 as the model approaches the (end) token.

We're excited to hear what people create! If you create a piece you like, you can upload it to a free service like Instaudio and then tweet us the link (the MuseNet demo has a tweet button to help with this).

If you're interested in learning more about OpenAI's music work, consider applying to join our team. Please feel free to email us with suggestions for the MuseNet demo. We'd also love to hear from you if you're interested in composing with MuseNet in more depth, or if you have MIDI files you'd like to add to the training set.

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We've created MuseNet, a deep neural network that can generate 4-minute musical compositions with 10 different instruments, and can combine styles from country to Mozart to the Beatles. MuseNet was not explicitly programmed with our understanding of music, but instead discovered patterns of harmony, rhythm, and style by learning to predict the next token in hundreds of thousands of MIDI files.

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https://www.gomlab.com/

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All about video, Multimedia software and services｜GOM Lab

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GOM Lab offers a wide range of multimedia software, mobile apps, and web services, from video player to video editor, such as GOM Player, GOM Mix Pro, etc.

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사진, 무료

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Semantris is a word association game powered by machine learning.

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Fast drawing for everyone. AutoDraw pairs machine learning with drawings from talented artists to help you draw stuff fast.

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Can a neural network learn to recognize doodles? See how well it does with your drawings and help teach it, just by playing.

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Play music together live on the web with this simple tool for remote teaching and collaboration.

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https://musiclab.chromeexperiments.com/Song-Maker/song/5162581852946432

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Chrome Music Lab - Song made Apr 14, 2022

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Check out this song created with Song Maker.

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Chrome Music Lab - Song made Apr 14, 2022

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Check out this song created with Song Maker.

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Chrome Music Lab - Song made Apr 14, 2022

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Check out this song created with Song Maker.

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Chrome Music Lab - Song made Apr 14, 2022

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Check out this song created with Song Maker.

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하이하이

HI

헬로우

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이쪽에 댓글달아주세요 :)
팀원:
김영진 / 한지수 / 장은영 / 손하은