Skip to content

Instantly share code, notes, and snippets.

View negrinho's full-sized avatar

Renato Negrinho negrinho

View GitHub Profile
@iacolippo
iacolippo / ray_deep_architect_ex2.py
Created April 24, 2020 10:07
Second example of using ray and deep_architect together - deepcopy protocol error
import os
import ray
from ray import tune
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
@nadavrot
nadavrot / Matrix.md
Last active May 8, 2024 18:53
Efficient matrix multiplication

High-Performance Matrix Multiplication

This is a short post that explains how to write a high-performance matrix multiplication program on modern processors. In this tutorial I will use a single core of the Skylake-client CPU with AVX2, but the principles in this post also apply to other processors with different instruction sets (such as AVX512).

Intro

Matrix multiplication is a mathematical operation that defines the product of

Visual Studio Code shortcuts I use often

Navigation

Sidebar:

  • Cmd-Shift-F: search
  • Cmd-Shift-E: files

Navigating in current editor:

@ragulpr
ragulpr / py
Last active December 7, 2020 10:24
Keras masking example
import keras
from keras.layers import *
from keras.models import Model
import theano as T
import tensorflow as tf
print('theano ver.',T.__version__)
print('tensorflow ver.',tf.__version__)
print('keras ver.',keras.__version__)
np.set_printoptions(precision=4)
np.random.seed(1)
@danijar
danijar / tf_char_rnn.py
Last active March 21, 2024 16:55
Simple character-level recurrent language model implemented in TensorFlow.
"""Character based language modeling with multi-layer GRUs.
To train the model:
python3 tf_char_rnn.py --mode training \
--logdir path/to/logdir --corpus path/to/corpus.txt
To generate text from seed words:
python3 tf_char_rnn.py --mode sampling \
@ivandrofly
ivandrofly / Unicode table
Created May 4, 2014 02:20
Unicode table - List of most common Unicode characters *
Unicode table - List of most common Unicode characters *
* This summary list contains about 2000 characters for most common ocidental/latin languages and most printable symbols but not chinese, japanese, arab, archaic and some unprintable.
Contains character codes in HEX (hexadecimal), decimal number, name/description and corresponding printable symbol.
What is Unicode?
Unicode is a standard created to define letters of all languages ​​and characters such as punctuation and technical symbols. Today, UNICODE (UTF-8) is the most used character set encoding (used by almost 70% of websites, in 2013). The second most used character set is ISO-8859-1 (about 20% of websites), but this old encoding format is being replaced by Unicode.
How to identify the Unicode number for a character?
Type or paste a character:
@debasishg
debasishg / gist:8172796
Last active May 7, 2024 22:18
A collection of links for streaming algorithms and data structures

General Background and Overview

  1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
  2. Models and Issues in Data Stream Systems
  3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
  4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
  5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t
@hellerbarde
hellerbarde / latency.markdown
Created May 31, 2012 13:16 — forked from jboner/latency.txt
Latency numbers every programmer should know

Latency numbers every programmer should know

L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns             
Compress 1K bytes with Zippy ............. 3,000 ns  =   3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns  =  20 µs
SSD random read ........................ 150,000 ns  = 150 µs

Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs

@jboner
jboner / latency.txt
Last active May 9, 2024 06:01
Latency Numbers Every Programmer Should Know
Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference 0.5 ns
Branch mispredict 5 ns
L2 cache reference 7 ns 14x L1 cache
Mutex lock/unlock 25 ns
Main memory reference 100 ns 20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy 3,000 ns 3 us
Send 1K bytes over 1 Gbps network 10,000 ns 10 us
Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD
@bortzmeyer
bortzmeyer / gist:1284249
Created October 13, 2011 13:42
The only simple way to do SSH in Python today is to use subprocess + OpenSSH...
#!/usr/bin/python
# All SSH libraries for Python are junk (2011-10-13).
# Too low-level (libssh2), too buggy (paramiko), too complicated
# (both), too poor in features (no use of the agent, for instance)
# Here is the right solution today:
import subprocess
import sys