datlife

ffmpeg -framerate 25 -i img%05d.jpg -c:v libx264 -profile:v high -crf 20 -pix_fmt yuv420p output.mp4

datlife

"""
Problem: 
--------
  We would like to explore which method perform row iteration in the most efficient way. 
  Create a data frammes with 3 columns and 100,000 rows
  
Results:
--------
vector:   Iterated over 100000 rows in 0.029180 | Sample at idx [0]: (1, 100000)
zip:      Iterated over 100000 rows in 0.073447 | Sample at idx [0]: [1, 100000]

datlife

Reference:

https://jany.st/post/2018-02-05-cross-compiling-tensorflow-for-jetson-tx1-with-bazel.html

datlife

Whether you're trying to give back to the open source community or collaborating on your own projects, knowing how to properly fork and generate pull requests is essential. Unfortunately, it's quite easy to make mistakes or not know what you should do when you're initially learning the process. I know that I certainly had considerable initial trouble with it, and I found a lot of the information on GitHub and around the internet to be rather piecemeal and incomplete - part of the process described here, another there, common hangups in a different place, and so on.

In an attempt to coallate this information for myself and others, this short tutorial is what I've found to be fairly standard procedure for creating a fork, doing your work, issuing a pull request, and merging that pull request back into the original project.

Creating a Fork

Just head over to the GitHub page and click the "Fork" button. It's just that simple. Once you've done that, you can use your favorite git client to clone your repo or j

datlife

"""Give a list of image filenames, this script compute the mean and std over all the images.

Requires:
 * OpenCV
 * tqdm

In this example, I use `Stanford Dogs Datasets" (~20k images)

Example Outputs: (8 cores CPU i7 4970K)
(env) dat@desktop:****/StanfordDogs$ python compute_mean_std.py 

datlife

"""Move Forward
Start from the first element in the array (A), index is i. Move forward by A[i] steps max.  
The algorithm is to return true/false, to indicate whether we can move from the first element
to the last element in the array
A = [2, 0, 1, 2, 0, 3] --> True

# Assumption:
-------------
    * A[i] > 0 and there are [1 ... N] possible steps A[i] can move if A[i] = N.

datlife

{
    // Use IntelliSense to learn about possible attributes.
    // Hover to view descriptions of existing attributes.
    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
    "version": "0.2.0",
    "configurations": [
        
        {
            "name": "(lldb) Debug binary_tree",
            "type": "cppdbg",

datlife

/**  
* MIT License
*
* Copyright (c) 2018  Dat Nguyen
* Trie (aka Prefix Tree) is a type of Tree Data Structure. A node contains a
* list of child Nodes.
*
* Applications:
* ============
*    * Word completion: quickly validate if a word is correctly type.

datlife

// Async should be first priority over callbacks and promises
//
// High level idea:
//   - Avoid .then .catch in promise
//   - Syntatic sugar to make async programming similar to sequential code
const axios = require('axios');
DEFAULT_URL = "https://jsonplaceholder.typicode.com"

async function getUser(id) {
    let resp = await axios.get(`${DEFAULT_URL}/users/${id}`)

datlife

Status: alpha

alias k=kubectl

# Add microsoft charts to heml
helm repo add msftcharts http://microsoft.github.io/charts/repo
helm repo update

# Launch spark cluster with custom config