深度學習

探索及應用生成對抗網路已額滿

探索及應用生成對抗網路

生成對抗網路 (Generative Adversarial Network, 簡稱 GAN) 是深度學習在 2017 年的最大焦點之一。 有大量的研究結果及讓人驚訝的應用。 單憑輪廓就能自動畫出生動的小貓、指定屬性就能生出成動漫角色、將圖片中的馬換成斑馬、將視訊中的直播主直接替換成另外一個人、從文字描述畫出圖片、 任意產生中文字型、改變照片中人的性別、膚色等等屬性。可以說不了解 GAN的話,等於錯過了一大半 2017 的深度學習的進展。但是:
  • 很像之前那個照片變梵谷畫風,挺好玩的,但用途似乎有限?
  • 好難訓練,為什麼實際訓練不出來?
  • 品質到什麼程度?有機會實用了嗎?
這些細節需要經過實做嘗試,才能具體掌握。本次課程除了介紹 GAN 外,也會從實際應用以及理論上的範例,探討其中的一些訣竅。
  1. 生成對抗網路簡介 我們會先介紹生成對抗網路的原理。
  2. 生成對抗網路的進展 我們將介紹 WGAN, improved WGAN, DRAGAN,  Pix2pix, CycleGAN 及後續的相關進展。
  3. 從論文到程式碼 凡是我不能創造的東西,我就不瞭解  –  理查·費曼 雖然論文中滿滿讓人讚嘆的原理和巧思,看完感感覺頗有收穫。 但沒真正寫一次,誰也無法確定了解多少。 可是,這麼多 GAN,要花多少時間來一個一個來實作?明明都照著 paper 寫了,為什麼還是黑畫面?
  4. 從程式碼到應用 有了可以跑的 code 後,要如何用在自己想要的資料上? GAN 不是萬靈丹,但在哪些條件下,能讓他發揮出最好的效果? 有什麼潛在的應用呢?
 

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增強學習初探以及最新發展趨勢已額滿

增強學習初探以及最新發展趨勢

增強學習 (Reinforcement Learning) 近年來搭配深度網路,讓電腦在學習打電動、下圍棋、以及操控機器人的應用上獲得超乎預期的成果。本課程從經典的 Markov Decision Process (MDP) 開始,先介紹傳統增強學習的算法,之後逐步講解著名的 Deep Q-Network (DQN) 以及 Policy Gradient (PG) 演算法。最後分享相關領域最新研究發展以及使用增強學習的小訣竅。    

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Emotion-AI: 運用人工智慧實現情緒辨識已額滿

Emotion-AI: 運用人工智慧實現情緒辨識

情緒的知覺與產生是人類的重要演化工具,藉以強化自身以及同物種成員生存的機會 - 對於仰賴社會互動與溝通以促進繁衍與生存的人類尤其重要。人類互動中的情緒訊號與內在情緒狀態會自然的驅動並反應於各種外顯行為中。Affective Computing (情感運算) 被 MIT Media Lab Rosalind Picard's 於 1995 年論文中提出,學術界中也開啟了近二十年相關技術研究以解碼情緒訊號並感知人類內部情感狀態。近年來,運用人工智慧計算聲音特徵與臉部表情為主的情緒辨識已為下一代與人相關的應用 (例如: 人機互動、教育、醫療、娛樂、商業等) 帶來了新的巨大機會。本課程將以運用人工智慧實現情緒辨識為主軸,內容包含簡介情感運算背景及其應用、情緒行為資料庫收集與處理、相關人工智慧技術、現今情緒辨識發展與未來。希望透過本課程提供的知識後,學員能更快速整合相關知識與工具來開發情緒辨識相關應用。本課程分成下列四大部分:
  1. 背景簡介情感運算及其應用 簡述人類內在情緒狀態與外顯行為之間的過往研究,並介紹近年來因情緒辨識技術成熟而產生的相關應用。
  2. 情緒行為資料庫收集與處理 介紹情緒多模態聲音影像資料庫收集的過程,並提供相關經驗與公開情緒資料庫分享。
  3. 情緒辨識人工智慧技術 簡介機器學習相關演算法來實現情緒辨識,並提供相關適用的工具可以加速上手。
  4. 情緒辨識的現在發展與未來 情緒辨識技術最新發展: 跨語言、多人互動、生理與外顯行為,並簡介情感運算與人類科學領域 (醫學、教育等) 之整合。

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手把手的深度學習實務已額滿

手把手的深度學習實務

手把手的深度學習實務深度學習 (Deep Learning) 是近年來備受重視的機器學習方法,現今許多語音與影像辨識的研究與應用,都基於深度學習的技術來完成。深度學習的工具不斷推陳出新,若你已經聽過這項技術,卻不知道該從何開始下手,這堂手把手的實務課程將會很適合你。本課程利用六個小時的時間,介紹 Keras 這個熱門的深度學習工具,從最簡單的前饋類神經網路 (Feedforward Neural Network) 開始,用 Keras 加入各種訓練技巧 (Regularization, Early Stopping, Dropout) 以得到好的預測模型。亦介紹深度學習模型的另一個變形:捲積式類神經網路 (Convolutional Neural Network, CNN),以完整的實務操作,讓你邁出成為深度學習訓練大師的第一步。

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What’s happening in deep learning已額滿

What’s happening in deep learning

近年深度學習受到極大的重視,整個領域在學界及業界都有蓬勃的發展,這場演講希望能跟聽眾分享近幾年這個領域的一些有趣發展。 首先會從近期的新模型與新方法談起,接著介紹深度學習在視覺、語音識別、自然語言處理領域上的突破,包含研究上與應用上的進展,最後會分享一些目前 TensorFlow 的功能與近況。        

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一日搞懂生成式對抗網路已額滿

一日搞懂生成式對抗網路

一日搞懂生成式對抗網路生成式對抗網路 (Generative Adversarial Network, GAN) 顯然是深度學習領域的下一個熱點,Yann LeCun 說這是機器學習領域這十年來最有趣的想法 (the most interesting idea in the last 10 years in ML),又說這是有史以來最酷的東西 (the coolest thing since sliced bread)。生成式對抗網路解決了什麼樣的問題呢?在機器學習領域,回歸 (regression) 和分類 (classification) 這兩項任務的解法人們已經不再陌生,但是如何讓機器更進一步創造出有結構的複雜物件 (例如:圖片、文句) 仍是一大挑戰。用生成式對抗網路,機器已經可以畫出以假亂真的人臉,也可以根據一段敘述文字,自己畫出對應的圖案,甚至還可以畫出二次元人物頭像 (左邊的動畫人物頭像就是機器自己生成的)。本課程希望能帶大家認識生成式對抗網路這個深度學習最前沿的技術。
  1. 60分鐘搞懂深度學習 你隔壁鄰居和他/她的兄弟姊妹都在用深度學習了,如果你還不會的話,用這個機會搞懂它吧。
  2. 什麼是生成式對抗網路 在生成式對抗網路中包含了兩個類神經網路:生成器 (Generator) 和鑑別器 (Discriminator) ,這兩個網路「寫作敵人,唸做朋友」,表面上彼此對抗、但實際上因為互相激勵所以越來越強。
  3. 生成式對抗網路的各種變形 原始的對抗網路訓練不易,於是有了種種改進的版本,例如: WGAN。生成式對抗網路相關技術不斷推陳出新,每次有新的技術就會在 GAN 前面再加幾個英文字母 (例如: WGAN、fGAN、BGAN、EBGAN、BEGAN 等等),英文字母很快就不夠用了。
  4. 生成式對抗網路和其它技術的關聯性 大家都知道 Alpha Go 背後使用了增強式學習 (Reinforcement Learning),使得這年頭有用到增強式學習就是潮,還有另外一個光聽名字就很潮的技術叫逆向增強式學習 (Inverse Reinforcement Learning),藉由了解這些技術和生成式對抗網路的關聯性把這些很潮的技術也學起來吧。
 

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手把手的深度學習實務已額滿

手把手的深度學習實務

手把手的深度學習實務深度學習 (Deep Learning) 是近年來備受重視的機器學習方法,現今許多語音與影像辨識的研究與應用,都基於深度學習的技術來完成。深度學習的工具不斷推陳出新,若你已經聽過這項技術,卻不知道該從何開始下手,這堂手把手的實務課程將會很適合你。本課程利用六個小時的時間,介紹 Keras 這個熱門的深度學習工具,從最簡單的前饋類神經網路 (Feedforward Neural Network) 開始,用 Keras 加入各種訓練技巧 (Regularization, Early Stopping, Dropout) 以得到好的預測模型。亦介紹深度學習模型的另一個變形:捲積式類神經網路 (Convolutional Neural Network, CNN),以完整的實務操作,讓你邁出成為深度學習訓練大師的第一步。

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深入淺出深度學習 (Dive into Deep Learning)已額滿

深入淺出深度學習 (Dive into Deep Learning)

深入淺出深度學習 (Dive into Deep Learning)深度學習 (Deep Learning) 是機器學習 (Machine Learning) 中近年來備受重視的一支,深度學習根源於類神經網路 (Artificial Neural Network) 模型,但今日深度學習的技術和它的前身已截然不同,目前最好的語音辨識和影像辨識系統都是以深度學習技術來完成。 你可能在很多不同的場合聽過各種用深度學習做出的驚人應用 (例如:最近紅遍大街小巷的 AlphaGo),聽完以後覺得心癢癢的,想要趕快使用這項強大的技術,卻不知要從何下手學習,以及不知道要如何應用深度學習於各式應用上,那這門課就是你所需要的。這門課程將由臺大資訊系陳縕儂教授利用短短的一天議程簡介機器學習及深度學習,包含深度學習基礎理論、模型各種變形、及深度學習應用的發展及趨勢。本課程希望幫助大家不只能了解深度學習,也可以了解如何應用深度學習於實際的應用問題上。無論是從未嘗試過深度學習的新手,還是已經有一點經驗想更深入了解,都可以在這門課中有所收穫。本課程由三個部分組成:
  1. 什麼是機器學習及深度學習 這堂課會簡介機器學習領域中不同的方法,以監督式學習 (Supervised Learning) 為主,並介紹機器學習及深度學習技術中包含的三個主要步驟:1) 設定模型架構、2) 定義目標函數、3) 選擇最好的函數。
  2. 深度學習模型之變形及應用 這堂課要講深度學習模型兩個常見的變形:捲積式類神經網路 (Convolutional Neural Network, CNN) 不只常用在影像辨識上,應用在圍棋上也成果驚人;循環式類神經網路 (Recurrent Neural Network, RNN) 讓深度學習模型有了記憶力,可以處理更複雜的問題。
  3. 深度學習發展及趨勢 – 非監督式學習 & 強化式學習 深度學習在監督式學習領域已經有較成熟的發展,但是在機器學習中其他領域都才剛起步。這堂課會介紹近期深度學習的最新趨勢,以及深度學習的研究者們近期在意的重要問題。主要介紹並討論非監督式學習 (Unsupervised Learning) 以及強化式學習 (Reinforcement Learning) 領域中深度學習的應用及其效果,並討論深度學習未來之發展。
 

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機器人視覺與深度學習應用已額滿

機器人視覺與深度學習應用

自動車是怎麼樣理解周遭的環境的?谷歌到底是怎麼快速找出我要的照片?波士頓的警方是怎麼找到馬拉松爆炸嫌疑犯的?這些都是由分析大量的照片與影音資料而來的。而這些視訊資料,不但資料量大,由電腦判讀更是不易,因此需要的技術與工具也相當的廣泛,從一開始的資料清理與判讀、抽象化資料萃取,和後端的視訊資料庫索引建立與查找,每一項都是極大的研究課題。深度學習技術之演進,更將其與所需之運算能量推升到空前的境界。在這一天的課程裡,我們集合了電腦視覺的概念簡介、多媒體資料的分類與檢索方式、深度學習於視訊資料的應用與挑戰、與加速這些技術的 GPU 平行算運算平台等,期由分析單筆視訊資料開始,深入淺出地引領您進入視訊資料的抽象美麗世界。最後的實作課程,並將介紹實際的應用例與實作所需之程式工具等,為理論與實務並重的安排。   

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手把手的深度學習實務已額滿

手把手的深度學習實務

深度學習 (Deep Learning) 是近年來備受重視的機器學習方法,現今許多語音與影像辨識的研究與應用,都基於深度學習的技術來完成。深度學習的工具不斷推陳出新,若你已經聽過這項技術,卻不知道該從何開始下手,這堂手把手的實務課程將會很適合你。本課程利用六個小時的時間,介紹 Keras 這個熱門的深度學習工具,從最簡單的前饋類神經網路 (Feedforward Neural Network) 開始,用 Keras 加入各種訓練技巧 (Regularization, Early Stopping, Dropout) 以得到好的預測模型。亦介紹深度學習模型的另一個變形:捲積式類神經網路 (Convolutional Neural Network, CNN),以完整的實務操作,讓你邁出成為深度學習訓練大師的第一步。  

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一天搞懂對話機器人已額滿

一天搞懂對話機器人

智慧對話機器人 (Intelligent Conversational Bot) 是人工智慧領域中近年來備受重視的一支,各個公司及個人都嘗試著架構專屬的聊天機器人 (ChatBot)。然而,聊天機器人的功能不僅僅侷限於聊天,能夠以對話的方式來協助人類完成各式各樣目標才是我們真正想要的人工智慧。聽完以後覺得心癢癢的,想要趕快建立一個自己的對話機器人,卻不知要從何下手學習,以及不知道要如何實作,那這門課就是你所需要的。 這門課程將由臺大資訊系陳縕儂教授利用短短的一天議程簡介對話系統,包含對話系統的基礎理論及架構、每個元件中如何實作、及對話系統近期的發展及趨勢。無論你是否有相關的經驗,都可以在這門課中有所收穫。
課程介紹
深入淺出深度學習 (Dive into Deep Learning)已額滿

深入淺出深度學習 (Dive into Deep Learning)

深度學習 (Deep Learning) 是機器學習 (Machine Learning) 中近年來備受重視的一支,深度學習根源於類神經網路 (Artificial Neural Network) 模型,但今日深度學習的技術和它的前身已截然不同,目前最好的語音辨識和影像辨識系統都是以深度學習技術來完成。 你可能在很多不同的場合聽過各種用深度學習做出的驚人應用 (例如:最近紅遍大街小巷的 AlphaGo),聽完以後覺得心癢癢的,想要趕快使用這項強大的技術,卻不知要從何下手學習,以及不知道要如何應用深度學習於各式應用上,那這門課就是你所需要的。 這門課程將由臺大資訊系陳縕儂教授利用短短的一天議程簡介機器學習及深度學習,包含深度學習基礎理論、模型各種變形、及深度學習應用的發展及趨勢。本課程希望幫助大家不只能了解深度學習,也可以了解如何應用深度學習於實際的應用問題上。無論是從未嘗試過深度學習的新手,還是已經有一點經驗想更深入了解,都可以在這門課中有所收穫。
課程介紹