| @ http://altdevblogaday.com/2011/08/06/demise-low-level-programmer/ | |
| Floating Point Numbers | |
| They are very useful but often used in situations where they simply don’t suit the solution the programmer is attempting to write. The following links should provide some background and info on where they are not so useful, what the pitfalls are and sometimes even how to avoid them. | |
| http://www.cprogramming.com/tutorial.html#fptutorial | |
| http://www.johndcook.com/blog/2009/04/06/numbers-are-a-leaky-abstraction/ | |
| http://www.codeproject.com/KB/recipes/float_point.aspx | |
| http://drdobbs.com/184402741?pgno=4 | |
| http://users.tkk.fi/jhi/infnan.html |
| %.hex: %.asm | |
| avra -fI $< | |
| rm *.eep.hex *.obj *.cof | |
| all: $(patsubst %.asm,%.hex,$(wildcard *.asm)) | |
| upload: ${program}.hex | |
| avrdude -c arduino -p m328p -P /dev/arduino-uno -b 115200 -U flash:w:$< | |
| monitor: |
Collection of License badges for your Project's README file.
This list includes the most common open source and open data licenses.
Easily copy and paste the code under the badges into your Markdown files.
Translations: (No guarantee that the translations are up-to-date)
This gist contains out.tex, a tex file that adds a PDF outline ("bookmarks") to the freely available pdf file of the book
The Elements of Statistical Learning (2nd ed), by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
https://web.stanford.edu/~hastie/ElemStatLearn/
The bookmarks allow to navigate the contents of the book while reading it on a screen.
| import time | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from torchvision import datasets | |
| from torchvision import transforms | |
| from torch.utils.data import DataLoader | |
| if torch.cuda.is_available(): |
| from matplotlib import animation | |
| import matplotlib.pyplot as plt | |
| import gym | |
| """ | |
| Ensure you have imagemagick installed with | |
| sudo apt-get install imagemagick | |
| Open file in CLI with: | |
| xgd-open <filelname> |
a conversation about the einops lib and why it's hard to optimize ML:
<PapuaHardyNet> the solution is obviously jax acc to shawn
which is why i'm thinking to also build a jax equivalent and compare all three: pytorch, pytorch + einops, jax. Let's see if I have the bandwidth to do so
<nshepperd2> einops is really cool imo
