We’re living through exciting times. The landscape of what computers can do is changing by the week. Tasks that only a few years ago were thought to require higher cognition are getting solved by machines at near-superhuman levels of per- formance. Tasks such as describing a photographic image with a sentence in idiom- atic English, playing complex strategy game, and diagnosing a tumor from a radiological scan are all approachable now by a computer. Even more impressively, computers acquire the ability to solve such tasks through examples, rather than human-encoded of handcrafted rules.
标签： Deep-Learning-with-PyTorch
上传时间： 2020-06-10
上传用户：shancjb
How to Think Like a Computer Scientist Learning with Python 学习linux下Python脚本的必备书籍
标签： Python Scientist Computer Learning
上传时间： 2014-10-29
上传用户：heart520beat
Machine Learning with WEKA: An Introduction （讲义） 关于数据挖掘和机器学习的.
标签： Introduction Learning Machine with
上传时间： 2013-12-27
上传用户：qq521
《How To Think Like A Computer Scientist Learning with C++》. Allen B. Downey写的关于c++的一本书。
标签： B. Scientist Computer Learning
上传时间： 2016-07-31
上传用户：
Matlab DSP learning with source code!
标签： learning Matlab source code
上传时间： 2017-02-16
上传用户：我们的船长
tell about plugin development in csharp, nice document for learning with sample code
标签： development document learning plugin
上传时间： 2017-04-09
上传用户：JasonC
this is a zip file contain a program for design of deep foundation with excel.
标签： foundation contain program design
上传时间： 2017-05-28
上传用户：变形金刚
Neural Networks and Deep Learning（简体中文），比较经典的深度学习入门教程。
标签： Networks Learning Neural Deep and 简体中文
上传时间： 2016-11-09
上传用户：zhousui
图像配准理论及算法研究.pdf cnn_tutorial.pdf Deep Learning（深度学习）学习笔记整理.pdf 00.神经⽹络与深度学习.pdf deep learning.pdf 深度学习方法及应用PDF高清晰完整版.pdf 斯坦福大学-深度学习基础教程.pdf 深度学习基础教程.pdf deep+learning.pdf 深度学习 中文版 ---文字版.pdf 神经网络与机器学习（原书第3版）.pdf
上传时间： 2013-06-07
上传用户：eeworm
The past decade has seen an explosion of machine learning research and appli- cations; especially, deep learning methods have enabled key advances in many applicationdomains,suchas computervision,speechprocessing,andgameplaying. However, the performance of many machine learning methods is very sensitive to a plethora of design decisions, which constitutes a considerable barrier for new users. This is particularly true in the booming field of deep learning, where human engineers need to select the right neural architectures, training procedures, regularization methods, and hyperparameters of all of these components in order to make their networks do what they are supposed to do with sufficient performance. This process has to be repeated for every application. Even experts are often left with tedious episodes of trial and error until they identify a good set of choices for a particular dataset.
标签： Auto-Machine-Learning-Methods-Sys tems-Challenges
上传时间： 2020-06-10
上传用户：shancjb
