GitHub - mozilla/TTS: :robot: Deep learning for Text to Speech (Discussion forum: https://discourse.mozilla.org/c/tts)You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session. Dismiss alert
The Best GPUs for Deep Learning in 2023 — An In-depth Analysis
OverviewThis blog post is structured in the following way. First, I will explain what makes a GPU fast. I will discuss CPUs vs GPUs, Tensor Cores, memory bandwidth, and the memory hierarchy of GPUs and how these relate to deep learning performance. These explanations might help you get a more intuitive sense of what to look for in a GPU. I discuss the unique features of the new NVIDIA RTX 40 Amper
Deep Learning Containers documentation  | Google Cloud
Deep Learning Containers are a set of Docker containers with key data science frameworks, libraries, and tools pre-installed. These containers provide you with performance-optimized, consistent environments that can help you prototype and implement workflows quickly. Learn more. Start your proof of concept with $300 in free credit Get access to Gemini 2.0 Flash Thinking Free monthly usage of popul
GitHub - microsoft/tensorwatch: Debugging, monitoring and visualization for Python Machine Learning and Data Science
TensorWatch is a debugging and visualization tool designed for data science, deep learning and reinforcement learning from Microsoft Research. It works in Jupyter Notebook to show real-time visualizations of your machine learning training and perform several other key analysis tasks for your models and data. TensorWatch is designed to be flexible and extensible so you can also build your own custo
GitHub - jantic/DeOldify: A Deep Learning based project for colorizing and restoring old images (and video!)
This Reposisitory is Archived This project was a wild ride since I started it back in 2018. 6 years ago as of this writing (October 19, 2024)!. It's time for me to move on and put this repo in the archives as I simply don't have the time to attend to it anymore, and frankly it's ancient as far as deep-learning projects go at this point! ~Jason Quick Start: The easiest way to colorize images using
iPhone Xã®Face IDã‚’ã€Pythonã¨ãƒ‡ã‚£ãƒ¼ãƒ—ラーニングã§å†ç¾ã™ã‚‹æ–¹æ³• | POSTD
iPhone Xã®æ–°ã—ã„ãƒãƒƒã‚¯è§£é™¤ãƒ¡ã‚«ãƒ‹ã‚ºãƒ ã®ãƒªãƒãƒ¼ã‚¹ã‚¨ãƒ³ã‚¸ãƒ‹ã‚¢ãƒªãƒ³ã‚° å…¨Pythonコードã¯ã“ã¡ã‚‰ã€‚ æ–°ã—ã„ iPhone X ã§è°è«–ã®çš„ã«ãªã£ãŸæ©Ÿèƒ½ã¨è¨€ãˆã°ã€æ–°è¦ãƒãƒƒã‚¯è§£é™¤æ–¹å¼ã§ã€Touch IDã®å¾Œç¶™ã§ã‚ã‚‹ Face ID ã§ã—ょã†ã€‚ ベゼルレスã®ã‚¹ãƒžãƒ¼ãƒˆãƒ•ã‚©ãƒ³ã‚’実ç¾ã—ã¦ããŸAppleã¯ã€ç«¯æœ«ã‚’ç°¡å˜ã‹ã¤ç´ æ—©ããƒãƒƒã‚¯è§£é™¤ã§ãã‚‹æ–°ãŸãªæ–¹å¼ã‚’開発ã™ã‚‹å¿…è¦ãŒã‚ã‚Šã¾ã—ãŸã€‚競åˆä»–社ãŒæŒ‡ç´‹èªè¨¼ã‚’採用ã—続ã‘ã‚‹ä¸ã€Appleã¯åˆ¥ã®ãƒã‚¸ã‚·ãƒ§ãƒ³ã®ãƒ¡ãƒ¼ã‚«ãƒ¼ã¨ã—ã¦ã‚¹ãƒžãƒ¼ãƒˆãƒ•ã‚©ãƒ³ã®ãƒãƒƒã‚¯è§£é™¤æ–¹å¼ã‚’刷新ã—ã€å¤‰é©ã‚’èµ·ã“ã™æ±ºæ–ã‚’ã—ã¾ã—ãŸã€‚ãã‚Œã¯ã€å˜ã«è¦‹ã‚‹ã ã‘ã§èªè¨¼ã•ã‚Œã‚‹ã¨ã„ã†æ©Ÿèƒ½ã§ã™ã€‚進化ã—ãŸï¼ˆãã—ã¦é©šãã»ã©å°ã•ã„） 深度センサ付ãカメラ ãŒå‰é¢ã«åŸ‹ã‚è¾¼ã¾ã‚Œã¦ã„ã‚‹ãŠã‹ã’ã§ã€iPhone Xã«ã¯ãƒ¦ãƒ¼ã‚¶ã®é¡”é¢ã®3Dマップを生æˆã™ã‚‹èƒ½åŠ›ãŒã‚ã‚Šã¾ã™ã€‚ã•ã‚‰ã«ã€ãƒ¦ãƒ¼ã‚¶ã®é¡”写真を 赤外線カメラ ã§ã‚ャプãƒãƒ£ã™ã‚‹ãŸã‚ã€ç’°å¢ƒã®å…‰ã‚„色彩
Google Researchã€è¤‡æ•°éŸ³ã‹ã‚‰ç‰¹å®šã®ç™ºè©±è€…ã ã‘ã®å£°ã‚’èžã“ãˆã‚‹ã‚ˆã†ã«ã™ã‚‹Deep learningを用ã„ãŸè¦–è´è¦šéŸ³å£°åˆ†é›¢ãƒ¢ãƒ‡ãƒ«ç™ºè¡¨
Google Researchã€è¤‡æ•°éŸ³ã‹ã‚‰ç‰¹å®šã®ç™ºè©±è€…ã ã‘ã®å£°ã‚’èžã“ãˆã‚‹ã‚ˆã†ã«ã™ã‚‹Deep learningを用ã„ãŸè¦–è´è¦šéŸ³å£°åˆ†é›¢ãƒ¢ãƒ‡ãƒ«ç™ºè¡¨ 2018-04-12 Google Researchã¯ã€Deep learningを用ã„ã¦ã€è¤‡æ•°ã®éŸ³ã‹ã‚‰1人ã®éŸ³å£°ã ã‘を抜ã出ã™è¦–è´è¦šéŸ³å£°åˆ†é›¢ãƒ¢ãƒ‡ãƒ«ã€ŒLooking to Listen at the Cocktail Partyã€ã‚’発表ã—ã¾ã—ãŸã€‚ 論文：Looking to Listen at the Cocktail Party: A Speaker-Independent Audio-Visual Model for Speech Separation 著者：Ariel Ephrat, Inbar Mosseri, Oran Lang, Tali Dekel, Kevin Wilson, Avinatan Hassidim, William T.
ディープラーニングã®é™ç•Œ | POSTD
(注：2017/04/08ã€ã„ãŸã ã„ãŸãƒ•ã‚£ãƒ¼ãƒ‰ãƒãƒƒã‚¯ã‚’å…ƒã«ç¿»è¨³ã‚’ä¿®æ£ã„ãŸã—ã¾ã—ãŸã€‚ @liaoyuanw ) ã“ã®è¨˜äº‹ã¯ã€ç§ã®è‘—書 『Deep Learning with Python（Pythonを使ã£ãŸãƒ‡ã‚£ãƒ¼ãƒ—ラーニング）〠（Manning Publications刊)ã®ç¬¬9ç« 2部を編集ã—ãŸã‚‚ã®ã§ã™ã€‚ç¾çŠ¶ã®ãƒ‡ã‚£ãƒ¼ãƒ—ラーニングã®é™ç•Œã¨ãã®å°†æ¥ã«é–¢ã™ã‚‹2ã¤ã®ã‚·ãƒªãƒ¼ã‚ºè¨˜äº‹ã®ä¸€éƒ¨ã§ã™ã€‚ æ—¢ã«ãƒ‡ã‚£ãƒ¼ãƒ—ラーニングã«æ·±ã親ã—ã‚“ã§ã„る人を対象ã«ã—ã¦ã„ã¾ã™ï¼ˆä¾‹ï¼šè‘—書ã®1ç« ã‹ã‚‰8ç« ã‚’èªã‚“ã 人）。èªè€…ã«ç›¸å½“ã®äºˆå‚™çŸ¥è˜ãŒã‚ã‚‹ã‚‚ã®ã¨æƒ³å®šã—ã¦æ›¸ã‹ã‚ŒãŸã‚‚ã®ã§ã™ã€‚ ディープラーニング：　幾何å¦çš„観察 ディープラーニングã«é–¢ã—ã¦ä½•ã‚ˆã‚Šé©šã‹ã•ã‚Œã‚‹ã®ã¯ã€ãã®ã‚·ãƒ³ãƒ—ルã•ã§ã™ã€‚10å¹´å‰ã¯ã€æ©Ÿæ¢°èªè˜ã®å•é¡Œã«ãŠã„ã¦ã€å‹¾é…é™ä¸‹æ³•ã§è¨“ç·´ã—ãŸã‚·ãƒ³ãƒ—ルãªãƒ‘ラメトリックモデルを使ã„ã€ã“ã‚Œã»ã©è¦‹äº‹ãªçµæžœã«åˆ°é”ã™ã‚‹ãªã©èª°ã‚‚想åƒã—ã¾ã›ã‚“ã§ã—ãŸã€‚
How to easily Detect Objects with Deep Learning on Raspberry Pi
How to easily Detect Objects with Deep Learning on Raspberry Pi This post demonstrates how you can do object detection using a Raspberry Pi. Like cars on a road, oranges in a fridge, signatures in a document and teslas in space. The real world poses challenges like having limited data and having tiny hardware like Mobile Phones and Raspberry Pis which can’t run complex Deep Learning models. This p
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How you can train an AI to convert your design mockups into HTML and CSS
By Emil Wallner Within three years, deep learning will change front-end development. It will increase prototyping speed and lower the barrier for building software. The field took off last year when Tony Beltramelli introduced the pix2code paper and Airbnb launched sketch2code. _Photo by [Unsplash](https://unsplash.com/photos/y0_vFxOHayg?utm_source=unsplash&utm_medium=referral&utm_content=creditCo
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Tweet ã‚ã‘ã¾ã—ã¦ãŠã‚ã§ã¨ã†ã”ã–ã„ã¾ã™ã€‚ 先日ã€MIT Technology Reviewã«ã“ã®ã‚ˆã†ãªè¨˜äº‹ãŒæŽ²è¼‰ã•ã‚Œã¦ã„ã¾ã—ãŸã€‚ 深層å¦ç¿’ã®éŽå¤§è©•ä¾¡ã¯å±é™ºã€ã‚¦ãƒ¼ãƒãƒ¼AIç ”ç©¶æ‰€ã®å‰æ‰€é•·ãŒæŒ‡æ‘˜ ã“ã®è«–文を発表ã—ãŸã®ã¯ãƒ‹ãƒ¥ãƒ¼ãƒ¨ãƒ¼ã‚¯å¤§å¦ã®å¿ƒç†å¦è€…ã®ã‚²ã‚¤ãƒªãƒ¼ãƒ»ãƒžãƒ¼ã‚«ã‚¹æ•™æŽˆã€‚心ç†å¦è€…ã¨ã„ã†ã“ã¨ã§ã€æˆ‘ã€…æƒ…å ±å·¥å¦ã®ç«‹å ´ã¨ã¯ã¾ãŸé•ã†ç«‹å ´ã§æ·±å±¤å¦ç¿’ã«ã§ãã‚‹ã“ã¨ã¨ã§ããªã„ã“ã¨ã‚’分離ã—ã¦ã„ã¾ã™ã€‚ ç†è€…ã¯ã“ã®ãƒ‹ãƒ¥ãƒ¼ã‚¹ã‚’見ã¦æœ€åˆã¯å発ã—ãŸã®ã§ã™ãŒã€åŽŸæ–‡ã‚’èªã‚“ã§ã¿ã‚‹ã¨ç¾çŠ¶ã®ãƒ‡ã‚£ãƒ¼ãƒ—ラーニングã®èª²é¡Œã«ã¤ã„ã¦ã‚ˆãã¾ã¨ã¾ã£ã¦ã„ã‚‹ã®ã§ã¯ãªã„ã‹ã¨æ€ã„ã¾ã—ãŸã®ã§ç´¹ä»‹ã—ã¾ã™ã€‚原文ã¯ã“ã¡ã‚‰ ■ディープラーニングã®é™ç•Œ マーカス教授ã«ã‚ˆã‚‹ã¨ã€ãƒ‡ã‚£ãƒ¼ãƒ—ラーニングã¯ã€Œç„¡é™ã®ãƒ‡ãƒ¼ã‚¿ã¨ç„¡é™ã®è¨ˆç®—資æºãŒã‚ã‚‹å ´åˆã«ãŠã„ã¦ã¯æ¥µã‚ã¦æœ‰åŠ¹ã§ã‚ã‚‹(In a world with infinite data, and infinite computati
AI in 2018 for developers
Hi again! In last article I tried to show my vision on what research areas are maturing and can grow big this year. Research is cool, but there must be something from AI world that became mature in 2017 and is ready now to be used in mass applications. This is what this article will be about — I would like to tell about technologies that are good enough to be used in your current work or to build
10 Alarming Predictions for Deep Learning in 2018
Photo by SpaceX on UnsplashI’ve got this ominous feeling that 2018 could be the year when everything just changes dramatically. The incredible breakthroughs we saw in 2017 for Deep Learning is going to carry over in a very powerful way in 2018. A lot of work coming from research will be migrating itself into everyday software applications. As I’ve done last year, here are my predictions for 2018.
AI and Deep Learning in 2017 – A Year in Review
The year is coming to an end. I did not write nearly as much as I had planned to. But I’m hoping to change that next year, with more tutorials around Reinforcement Learning, Evolution, and Bayesian Methods coming to WildML! And what better way to start than with a summary of all the amazing things that happened in 2017? Looking back through my Twitter history and the WildML newsletter, the followi
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(Photo by Pixabay) ã“ã‚Œã¯ãŸã ã®å¹´æœ«ãƒã‚¨ãƒ ã§ã™ã€‚何ã²ã¨ã¤ã¨ã—ã¦é«˜åº¦ã«æŠ€è¡“çš„ãªè©±ã‚‚ãªã‘ã‚Œã°ã€ãŸã‚ã«ãªã‚‹è©±ã‚‚ã‚ã‚Šã¾ã›ã‚“ã®ã§äºˆã‚ã”了承ãã ã•ã„。 時ãŒæµã‚Œã‚‹ã®ã¯æ—©ã„ã‚‚ã®ã§ã€åƒ•ãŒã‚¤ãƒ³ãƒ€ã‚¹ãƒˆãƒªãƒ¼ã«ãŠã‘るデータ分æžã®ä»•äº‹ã‚’手ãŒã‘るよã†ã«ãªã£ã¦ã‹ã‚‰5å¹´ç›®ã®ä»Šå¹´ã‚‚ã»ã©ãªã終ã‚ã‚ã†ã¨ã—ã¦ã„ã¾ã™ã€‚上記ã®è¨˜äº‹ã§ã¯ãã®é–“ã«ã‚ã£ãŸæ§˜ã€…ãªå‡ºæ¥äº‹ã‚’振り返りã¾ã—ãŸãŒã€ä»Šå›žã¯ç¾åœ¨ã®ä»•äº‹ã®ã‚„ã‚Šæ–¹ã«ã¤ã„ã¦æœ€è¿‘æ„Ÿã˜ã¦ã„ã‚‹ã“ã¨ã‚’徒然ãªã‚‹ã¾ã¾ã«æ›¸ã散らã—ã¦ã¿ã‚ˆã†ã‹ã¨æ€ã„ã¾ã™ã€‚ 想åƒã‚’超ãˆã¦é¥ã‹ã«é€²ã‚“ã§ã„ã「最先端〠今ã®æ¥ç•Œ*1ã§æœ€å…ˆç«¯ã¨è¨€ãˆã°ä¸€èˆ¬ã«ã¯Deep Learningã¨ã‹ã€‡ã€‡Netã®ã“ã¨ã‚’指ã™ã“ã¨ãŒå¤šã„ã§ã™ã—ã€ä»¥å‰ã€Œä»Šã®çŠ¶æ³ã¯ã€Žä¿ºãŒè€ƒãˆãŸæœ€å¼·ã®ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯é¸æ‰‹æ¨©ã€ã ã€ã¨è¨€ã£ãŸé€šã‚Šã®æœ‰æ§˜ã ã¨å€‹äººçš„ã«ã¯èªè˜ã—ã¦ã„ã¾ã™*2。 ãã®æ„味ã§ã¯ä»Šå¹´ã‚‚最先端ã®ç ”究開発ã®é€²åŒ–ã®ã‚¹ãƒ”ードã¯ã¨ã©ã¾ã‚‹ã“ã¨ã‚’知らãªã„ã¨ã„ã†å°è±¡ã§
ObamaNet: Photo-realistic lip-sync from text
ObamaNet: Photo-realistic lip-sync from text Rithesh Kumar, Jose Sotelo, Kundan Kumar, Alexandre de Brebisson, Yoshua Bengio Paper Abstract We present ObamaNet, the first architecture that generates both audio and synchronized photo-realistic lip-sync videos from any new text. Contrary to other published lip-sync approaches, ours is only composed of fully trainable neural modules and does not rely
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GitHub - lucidrains/deep-daze: Simple command line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). Technique was originally created by https://twitter.com/advadnoun
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AI-Powered Intelligent Document Processing & Workflow Automation
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