The amount of data available to us is growing rapidly, but what is required to make useful conclusions out of it?
Outline
1. Different tactics to gather your data
2. Cleansing, scrubbing, correcting your data
3. Running analysis for your data
4. Bring your data to live with visualizations
5. Publishing your data for rest of us as linked open data
Deep Auto-Encoder Neural Networks in Reiforcement Learnning (第 9 回 Deep Learn...Ohsawa Goodfellow
Deep Learning Japan @ 東大です
http://www.facebook.com/DeepLearning
https://sites.google.com/site/deeplearning2013/
On the Necessity and Inapplicability of PythonTakeshi Akutsu
This document discusses the use of Python for numerical software development. It begins by introducing the author and their background in computational mechanics. It then discusses PyHUG, the Python user group in Taiwan, and PyCon Taiwan 2020.
The document notes that while Python is slow for number crunching, NumPy can provide reasonably fast performance. It explains that a hybrid architecture is commonly used, with the core computing kernel written in C++ for speed and Python used for the user-level API to describe complex problems more easily. An example of solving the Laplace equation is provided to demonstrate the speed differences between pure Python, NumPy, and C++ implementations.
The document advocates for training computer scientists in a hybrid approach through a numerical software