人工智慧課程

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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課程介紹
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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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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無所不在的自然語言處理—基礎概念、技術與工具介紹已額滿

無所不在的自然語言處理—基礎概念、技術與工具介紹

無所不在的自然語言處理—基礎概念、技術與工具介紹自然語言處理 (Natural Language Processing) 技術專門對付非結構性、沒有整理成欄位值的資料,這也是資料分析時的燙手山芋之一。能夠理解語意,讓機器使用自然語言溝通,就能夠通過人工智慧的終極測試 — 圖靈測試 (Turing Test),可見這個問題的確是個極大的挑戰。即使是自然語言的基礎技術,例如怎麼樣從文章中抽取出重要的資訊?怎麼樣分析句子的結構?怎麼樣找到語言中所表達的語意?怎麼樣處理不同的語言?這些技巧在資料分析時不僅必備,想要做得好更需要訣竅。本課程先從基礎介紹開始,希望能帶大家認識自然語言處理這個在資料分析與資料探勘時非常有用的技術,並燃起對自然語言處理的興趣及熱情。1. 什麼是自然語言處理 從自然語言的基礎定義與各個相關議題談起,在 60 分鐘內畫出自然語言處理技術的重點,包括可以處理的問題,以及目前的技術進展等。2. 中英文文本處理相關工具與資源介紹 知道某些問題可以用自然語言處理的技術來進行,或是拿到資料後的下一步該如何?這裡介紹各式各樣自然語言處理最優的開發工具,包括英文與中文。3. 自然語言處理於網路與社群媒體的挑戰 處理一般的文字資料還不夠,還要考慮從網路收集來的文字資料,既多又免費!但是網路文字有其特殊的問題需要解決,同時網路文字經常可以結合其他元資訊一併處理得到更佳的結果!我們將會介紹跨入網路文字處理時可能遭遇的問題與解決方式,同時說明基本的文字與其他資料的結合方式。4. 自然語言處理發展趨勢與業界應用 在人工智慧的大趨勢下,自然語言處理自然也是很重要的一環,這個技術未來的發展趨勢與應用方向在哪裡呢?我們將會介紹目前產業界對於那些技術有所需求,有哪些潛在的應用,以及未來自然語言處理可以與那些領域結合更加發揮它的力量。

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今天開始打造人工智慧團隊-給企業主及團隊領導者的經驗談,解析所有你將遇到的已發生,未發生,將發生的問題及陷阱已額滿

今天開始打造人工智慧團隊-給企業主及團隊領導者的經驗談,解析所有你將遇到的已發生,未發生,將發生的問題及陷阱

今天開始打造人工智慧團隊-給企業主及團隊領導者的經驗談,解析所有你將遇到的已發生,未發生,將發生的問題及陷阱最近的科技新聞熱點在人工智慧,街頭巷口人人都在談人工智慧,因為已證明在許多產業應用上的價值,讓許多企業趨之若騖,不論本來談不談/做不做資料科學及大數據研發及導入,今年開始都開始談人工智慧。他們可能說,我們已從大數據階段「進化」到人工智慧,比其它還在做大數據的公司還「先進」,這是種有趣的說法。事實上,大數據、機器學習及人工智慧是不可分割的。大數據是資訊來源,機器學習是資訊的處理方法,藉以淬取出複雜的規則,讓電腦展現出擬似人類智慧的行為,我們稱為人工智慧。換句話說,以今天的技術來說,大數據及機器學習是發展人工智慧的必要條件;在人工智慧技術領先的企業,同時也必然是大數據及機器學習的領先者,沒有人能跳過這兩者而直接開發出先進的人工智慧系統。因此,若想要導入人工智慧,過去欠的技術債,今天必須先還清。企業裡的資料基礎建設 (例如資料倉儲) 建好了嗎?資料管理有個統一的授權模式嗎?或是依舊得讓資料使用者一一去說服企業中分散各處的資料擁有者?資料的收集及產出有融入企業流程中嗎?還是每次得為了特定的專案再花人力時間資源來整理收集?資料應用的導入是預設得進行的常規任務,還是得另外花力氣去說服所有相關單位?分析資料的團隊是管理資料的團隊兼任,還是有個專業的資料分析團隊來進行?同時,所有的資料應用者都熟悉機器學習嗎?還是以傳統繪圖製表的方式在做基礎層次的資料視覺化?更直白的說法是,千萬不要以為公司有資料倉儲,打開電腦就有報表可以看,公司同仁開會討論時都是基於漂亮的即時圖表,就是把大數據應用的很好的公司。事實可能剛好相反,最淺層的資料應用是讓老板每天看圖表;最最深入的資料應用應用是隱形的,看不到圖表。不然你覺得 AlphaGo 是看著一堆圖表學到如何下棋的嗎?透過機器學習從資料淬取規則出來,而那些規則因為過於複雜,所以根本無法完整地視覺化呈現,只能以人工智慧系統來直接應用這些規則,讓系統根據當前的輸入值提供最佳的輸出值。過去幾年,我們在資料的蒐集及解讀上很熱衷,對於機器學習這種以看不見圖表的方式來分析資料及發展人工智慧應用有點後知後覺。十分期待本土各種產業在覺知後的奮起直追,特別在我們有特殊利基點的產業(如製造業、農業、生醫產業等)及社會領域(施政、公用事業、教育等)上,以真正踏實的人工智慧發展來改善社會及產業體質。在此課程中,我將為聽眾闡明資料科學、大數據、人工智慧、機器學習/深度學習等相近但又不同的詞彙,再以各領域的實際案例來分享資料的可能應用及實用價值。同時,我將與聽眾分享其協助多家企業培訓資料科學家及導入資料科學/人工智慧團隊的各種經驗,各種常見的問題以及如何解決這些問題的有效方式。如何選才/育才,才能讓團隊擁有最堅實的戰力以高效率來解決問題。如何選題,才能快速展現團隊的價值等等。最後,我將探討台灣發展人工智慧的挑戰及契機,以及企業應用人工智慧進行競爭力升級的方式及各種關鍵議題。

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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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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。
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人工智慧與機器學習在推薦系統上的應用已額滿

人工智慧與機器學習在推薦系統上的應用

本課程特別邀請到臺大資工系林守德教授,利用六小時的課程簡介人工智慧的歷史演進以及其與機器學習的關聯,同時也會介紹在實務上如何訓練好的機器學習模型,及如何應用相關技術建立智慧推薦系統。本課程由四個部分組成:
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  • 內容為本推薦系統
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