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.
標簽： Deep-Learning-with-PyTorch
上傳時間： 2020-06-10
上傳用戶：shancjb
How to Think Like a Computer Scientist Learning with Python 學習linux下Python腳本的必備書籍
標簽： Python Scientist Computer Learning
上傳時間： 2014-10-29
上傳用戶：heart520beat
Machine Learning with WEKA: An Introduction （講義） 關于數據挖掘和機器學習的.
標簽： Introduction Learning Machine with
上傳時間： 2013-12-27
上傳用戶：qq521
《How To Think Like A Computer Scientist Learning with C++》. Allen B. Downey寫的關于c++的一本書。
標簽： B. Scientist Computer Learning
上傳時間： 2016-07-31
上傳用戶：
Matlab DSP learning with source code!
標簽： learning Matlab source code
上傳時間： 2017-02-16
上傳用戶：我們的船長
tell about plugin development in csharp, nice document for learning with sample code
標簽： development document learning plugin
上傳時間： 2017-04-09
上傳用戶：JasonC
this is a zip file contain a program for design of deep foundation with excel.
標簽： foundation contain program design
上傳時間： 2017-05-28
上傳用戶：變形金剛
Neural Networks and Deep Learning（簡體中文），比較經典的深度學習入門教程。
標簽： Networks Learning Neural Deep and 簡體中文
上傳時間： 2016-11-09
上傳用戶：zhousui
圖像配準理論及算法研究.pdf cnn_tutorial.pdf Deep Learning（深度學習）學習筆記整理.pdf 00.神經?絡與深度學習.pdf deep learning.pdf 深度學習方法及應用PDF高清晰完整版.pdf 斯坦福大學-深度學習基礎教程.pdf 深度學習基礎教程.pdf deep+learning.pdf 深度學習 中文版 ---文字版.pdf 神經網絡與機器學習（原書第3版）.pdf
上傳時間： 2013-06-07
上傳用戶：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.
標簽： Auto-Machine-Learning-Methods-Sys tems-Challenges
上傳時間： 2020-06-10
上傳用戶：shancjb
