dergachev

OS X Screencast to animated GIF

This gist shows how to create a GIF screencast using only free OS X tools: QuickTime, ffmpeg, and gifsicle.

Instructions

To capture the video (filesize: 19MB), using the free "QuickTime Player" application:

jmindek

distinct column -> For each row returned, return only the unique members of a set. Think of it as for each row in a projection, concatenate all the column values and return only the strings that are unique.

test_db=# SELECT DISTINCT parent_id, child_id, id FROM test.foo_table ORDER BY parent_id, child_id, id LIMIT 10;
parent_id | child_id | id
-----------+------------+-----------------------------
1000040 | 103 | 1000040|2645405726|0001|103

alexeygrigorev

graph = tf.Graph()

with graph.as_default():
    with graph.device('/gpu:0'):
        # input data
        train_dataset = tf.placeholder(tf.int32, shape=[batch_size])
        train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
        valid_dataset = tf.constant(valid_examples, dtype=tf.int32)

        # variables

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

hollisn

### Tensorflow 
# cuda 
# download cuda 8.0 and cd to the dir
sudo dpkg -i cuda-repo-ubuntu1604_8.0.44-1_amd64.deb
sudo apt-get update
sudo apt-get install cuda
# cuda toolkit (optional?)
sudo apt install nvidia-cuda-toolkit
nvcc --version
# cudnn

hollisn

sudo apt-get install htop
sudo apt-get install git
sudo apt-get install ntfs-3g
sudo apt-get install python-gpgme
sudo apt-get install vlc browser-plugin-vlc

# printer 
sudo ln -s /etc/init.d/cupsys /etc/init.d/lpd

omoindrot

"""
Example TensorFlow script for finetuning a VGG model on your own data.
Uses tf.contrib.data module which is in release v1.2
Based on PyTorch example from Justin Johnson
(https://gist.github.com/jcjohnson/6e41e8512c17eae5da50aebef3378a4c)

Required packages: tensorflow (v1.2)
Download the weights trained on ImageNet for VGG:
```
wget http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz