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。
This document provides an introduction to exploring and visualizing data using the R programming language. It discusses the history and development of R, introduces key R packages like tidyverse and ggplot2 for data analysis and visualization, and provides examples of reading data, examining data structures, and creating basic plots and histograms. It also demonstrates more advanced ggplot2 concepts like faceting, mapping variables to aesthetics, using different geoms, and combining multiple geoms in a single plot.
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。
This document provides an introduction to exploring and visualizing data using the R programming language. It discusses the history and development of R, introduces key R packages like tidyverse and ggplot2 for data analysis and visualization, and provides examples of reading data, examining data structures, and creating basic plots and histograms. It also demonstrates more advanced ggplot2 concepts like faceting, mapping variables to aesthetics, using different geoms, and combining multiple geoms in a single plot.
This document is a presentation by Ted Chang about creating new opportunities for Taiwan's intelligent transformation. It discusses paradigm shifts in technology such as mobile phones and cloud computing. It introduces concepts like the Internet of Things, artificial intelligence, and how they can be combined. It argues that key driving forces for the future will be machine learning, big data, cloud computing and AI. The presentation envisions applications of these technologies in areas like future medicine and smart manufacturing. It ends by emphasizing the importance of wisdom and intelligence in shaping the future.
- The document discusses how artificial intelligence can enable earlier and safer medicine.
- It provides background on the author and their expertise in biomedical informatics and roles as editor-in-chief of several academic journals.
- Key applications of AI in healthcare discussed include using machine learning on large medical datasets to detect suspicious moles earlier, reduce medication errors, and more accurately predict cancer occurrence up to 12 months in advance.
- The author argues that AI has the potential to transform medicine by enabling more preventive and earlier detection approaches compared to traditional reactive healthcare models.
Jane may be able to help. Let me check with her personal assistant Jane-ML.
NextPrevIndex
Meera checks with Jane-ML
User-Agent Interaction (V)
48
PA_Meera: Mina, do you
have trouble in
debugging?
Mina: Yes, is there
anyone who has done
this?
Personal Agent
[Meera]
Jane-ML: Jane has done a similar debugging problem before. She is available now and willing to help.
compiletheme
Compiling output
1) Kaggle is the largest platform for AI and data science competitions, acquired by Google in 2017. It has been used by companies like Bosch, Mercedes, and Asus for challenges like improving production lines, accelerating testing processes, and component failure prediction.
2) The document discusses the author's experiences winning silver medals in Kaggle competitions involving camera model identification, passenger screening algorithms, and pneumonia detection. For camera model identification, the author used transfer learning with InceptionResNetV2 and high-pass filters to identify camera models from images.
3) For passenger screening, the author modified a 2D CNN to 3D and used 3D data augmentation to rank in the top 7% of the $1
[台灣人工智慧學校] Bridging AI to Precision Agriculture through IoT台灣資料科學年會
The document describes a system for precision agriculture using IoT. It involves sensors collecting environmental data from fields and feeding it to a control board connected to actuators like irrigation systems. The data is also sent to an IoTtalk engine and AgriTalk server in the cloud for analysis and remote access/control through an AgriGUI interface. Equations were developed to estimate nutrient levels like nitrogen from sensor readings to help optimize crop growth.
The document discusses Open Robot Club and includes several links to its website and YouTube videos. It provides information on the club's computing resources like NVIDIA V100 GPUs. Tables with metrics like underkill and overkill percentages are included for different types of tasks like AI AOI and PCB inspection. The club's website and demos are referenced throughout.
This document is a presentation by Ted Chang about creating new opportunities for Taiwan's intelligent transformation. It discusses paradigm shifts in technology such as mobile phones and cloud computing. It introduces concepts like the Internet of Things, artificial intelligence, and how they can be combined. It argues that key driving forces for the future will be machine learning, big data, cloud computing and AI. The presentation envisions applications of these technologies in areas like future medicine and smart manufacturing. It ends by emphasizing the importance of wisdom and intelligence in shaping the future.
- The document discusses how artificial intelligence can enable earlier and safer medicine.
- It provides background on the author and their expertise in biomedical informatics and roles as editor-in-chief of several academic journals.
- Key applications of AI in healthcare discussed include using machine learning on large medical datasets to detect suspicious moles earlier, reduce medication errors, and more accurately predict cancer occurrence up to 12 months in advance.
- The author argues that AI has the potential to transform medicine by enabling more preventive and earlier detection approaches compared to traditional reactive healthcare models.
Jane may be able to help. Let me check with her personal assistant Jane-ML.
NextPrevIndex
Meera checks with Jane-ML
User-Agent Interaction (V)
48
PA_Meera: Mina, do you
have trouble in
debugging?
Mina: Yes, is there
anyone who has done
this?
Personal Agent
[Meera]
Jane-ML: Jane has done a similar debugging problem before. She is available now and willing to help.
compiletheme
Compiling output
1) Kaggle is the largest platform for AI and data science competitions, acquired by Google in 2017. It has been used by companies like Bosch, Mercedes, and Asus for challenges like improving production lines, accelerating testing processes, and component failure prediction.
2) The document discusses the author's experiences winning silver medals in Kaggle competitions involving camera model identification, passenger screening algorithms, and pneumonia detection. For camera model identification, the author used transfer learning with InceptionResNetV2 and high-pass filters to identify camera models from images.
3) For passenger screening, the author modified a 2D CNN to 3D and used 3D data augmentation to rank in the top 7% of the $1
[台灣人工智慧學校] Bridging AI to Precision Agriculture through IoT台灣資料科學年會
The document describes a system for precision agriculture using IoT. It involves sensors collecting environmental data from fields and feeding it to a control board connected to actuators like irrigation systems. The data is also sent to an IoTtalk engine and AgriTalk server in the cloud for analysis and remote access/control through an AgriGUI interface. Equations were developed to estimate nutrient levels like nitrogen from sensor readings to help optimize crop growth.
The document discusses Open Robot Club and includes several links to its website and YouTube videos. It provides information on the club's computing resources like NVIDIA V100 GPUs. Tables with metrics like underkill and overkill percentages are included for different types of tasks like AI AOI and PCB inspection. The club's website and demos are referenced throughout.