深度學習神課斯坦福 cs231n deep learning open course from Standford 英文教學版(DVD9一片裝 此片售價200元)




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軟體名稱:深度學習神課斯坦福 cs231n deep learning open course from Standford 英文教學版(DVD9一片裝 此片售價200元)
語系版本:英文教學版
光碟片數:單片裝
破解說明:
系統支援:Windows 7/XP/Vista
軟體類型:電腦教學
硬體需求:PC
更新日期:2017-11-28
官方網站:
中文網站:
軟體簡介:
銷售價格:$200元
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軟體簡介:
　
cs231n可以說是深度學習最好的公開課，這個版本是spring 2017。新鮮出爐，歡迎
各位品嚐

Computer Vision has become ubiquitous in our society, with applications in 
search, image understanding, apps, mapping, medicine, drones, and self-driving 
cars. Core to many of these applications are visual recognition tasks such as 
image classification, localization and detection. Recent developments in neural 
network (aka 「deep learning」) approaches have greatly advanced the performance 
of these state-of-the-art visual recognition systems. This course is a deep dive 
into details of the deep learning architectures with a focus on learning end-to-
end models for these tasks, particularly image classification. During the 10-week 
course, students will learn to implement, train and debug their own neural networks 
and gain a detailed understanding of cutting-edge research in computer vision. The 
final assignment will involve training a multi-million parameter convolutional 
neural network and applying it on the largest image classification dataset (ImageNet). 
We will focus on teaching how to set up the problem of image recognition, the 
learning algorithms (e.g. backpropagation), practical engineering tricks for training 
and fine-tuning the networks and guide the students through hands-on assignments 
and a final course project. Much of the background and materials of this course 
will be drawn from the ImageNet Challenge.

課程名稱：
Lecture 1：計算機視覺的概述、歷史背景以及課程計劃
Lecture 2：圖像分類算法
Lecture 3：損失函數和優化（loss Function and optimization）
Lecture 4：神經網絡
Lecture 5：卷積神經網絡（CNN）
Lecture 6：如何訓練神經網絡 I
 Lecture 7：如何訓練神經網絡 II
 Lecture 8: 深度學習軟件基礎
Lecture 9：卷積神經網絡架構（CNN Architectures）
Lecture 10：循環神經網絡（Recurrent Neural Networks）
Lecture 11：檢測與分割（Detection and Segmentation）
Lecture 12：可視化和理解（Visualizing and Understanding）
Lecture 13：生成模型（Generative Models）
Lecture 14：強化學習（Reinforcement Learning）
Lecture 15：深度學習高效的方法和硬件（Efficient Methods and Hardware for Deep Learning）
Lecture 16：對抗性樣本和對抗性訓練（Adversarial Examples and Adversarial Training）


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