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。
Exploratory data analysis is the process of quickly looking at data, formulating hypotheses, and testing those hypotheses. In practice, two of the most important components of this process are transforming data and visualizing it. This tutorial will be a hands-on, practical introduction to using R for data exploration, with an emphasis on data transformation and visualization. I will focus on using modern R packages like ggplot2, dplyr, and tidyr for this tutorial.
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。
Exploratory data analysis is the process of quickly looking at data, formulating hypotheses, and testing those hypotheses. In practice, two of the most important components of this process are transforming data and visualizing it. This tutorial will be a hands-on, practical introduction to using R for data exploration, with an emphasis on data transformation and visualization. I will focus on using modern R packages like ggplot2, dplyr, and tidyr for this tutorial.
13. Recognition Tolerance è Data Augmentation
Neural Normalization è Batch Normalization
Synaptic Pruning è Weight Decay
Sparse Coding è Dropout
設計CNN (2/2)
CNN在許多調教技巧也是模仿神經系統