philschmid

Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output.
- Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure.
- Reasoning Before Conclusions: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS!
    - Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed.
    - Conclusion, classifications, or results should ALWAYS appear last.
- Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements.
   - What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from p

nigeljyng

from keras import backend as K
from keras.engine import InputSpec
from keras.engine.topology import Layer
import numpy as np


class TemporalMaxPooling(Layer):
    """
    This pooling layer accepts the temporal sequence output by a recurrent layer
    and performs temporal pooling, looking at only the non-masked portion of the sequence.

barbolo

References:

https://github.com/tesseract-ocr/tesseract/wiki/Compiling

http://stackoverflow.com/questions/33588262/tesseract-ocr-on-aws-lambda-via-virtualenv

https://github.com/sirfz/tesserocr

Instructions for running Tesseract OCR on AWS Lambda with Python

morgangiraud

# Remove anything linked to nvidia
sudo apt-get remove --purge nvidia*
sudo apt-get autoremove

# Search for your driver
apt search nvidia

# Select one driver (the last one is a decent choice)
sudo apt install nvidia-370

wangruohui

In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install NVIDIA Graphics Driver via runfile
  • Remove Previous Installations (Important)
  • Download the Driver
  • Install Dependencies
  • Creat Blacklist for Nouveau Driver

• Stop lightdm/gdm/kdm

danijar

# Working example for my blog post at:
# https://danijar.github.io/structuring-your-tensorflow-models
import functools
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data


def doublewrap(function):
    """
    A decorator decorator, allowing to use the decorator to be used without

kylemcdonald

#!/usr/bin/env python

import argparse
import matplotlib.pyplot as plt
import librosa
import numpy as np
import os
from progressbar import ProgressBar

parser = argparse.ArgumentParser(

jbn

# Installation: 
# - Put this in your .bash_profile
# - Replace my directory with your exobrain directory on line 10
#
# What you get:
#    A Jupyter notebook open on a well-defined port that is always
#    on for exobrain-ing your ideas...

exec 6<>/dev/tcp/localhost/10000 || (
    source activate py27 && 

kidpixo

Warning This is SEVERELY outdated, the current jupyter version is > 6.X, please refer to your current jupyter notebook installation!

Disclaimer : I just copied those shortcuts from Jupyter Menú > Help > Keyboard Shortcuts, I didn't wrote them myself.

Check your current shortcuts in your Help, shortcuts coule have been modified by extensions or your past self.

Toc

Keyboard shortcuts

ramhiser

import pandas as pd
import numpy as np
from sklearn.feature_extraction import DictVectorizer
 
def encode_onehot(df, cols):
    """
    One-hot encoding is applied to columns specified in a pandas DataFrame.
    
    Modified from: https://gist.github.com/kljensen/5452382