Algogator

Below are the Big O performance of common functions of different Java Collections.


List                 | Add  | Remove | Get  | Contains | Next | Data Structure
---------------------|------|--------|------|----------|------|---------------
ArrayList            | O(1) |  O(n)  | O(1) |   O(n)   | O(1) | Array
LinkedList           | O(1) |  O(1)  | O(n) |   O(n)   | O(1) | Linked List
CopyOnWriteArrayList | O(n) |  O(n)  | O(1) |   O(n)   | O(1) | Array

Algogator

# numCourses = 4, prerequisites = [[1,0],[2,0],[3,1],[3,2]]

from collections import defaultdict, deque
class Solution:

    def findOrder(self, numCourses, prerequisites):
        """
        :type numCourses: int
        :type prerequisites: List[List[int]]
        :rtype: List[int]

Algogator

pointer var = address

* = &

int val = 10;
int *p;
p = &val;

// *p is val p is pointing to
// p is address inside p

Algogator

1. https://medium.com/leetcode-patterns
2. https://jeremyaguilon.me/blog/a_collection_of_whiteboard_interview_templates
3. https://hackernoon.com/14-patterns-to-ace-any-coding-interview-question-c5bb3357f6ed

Algogator

cmake_minimum_required(VERSION 2.8.4)
project(lotd)

find_package(PkgConfig REQUIRED)

pkg_search_module(EVENT REQUIRED libevent)

set(libname "config")

file(GLOB SRC

Algogator

cmake_minimum_required(VERSION 2.8.12)
project(lotd)

add_definitions("-std=c++11")

set(libname "config")

include(${CMAKE_BINARY_DIR}/conanbuildinfo.cmake)
conan_basic_setup()

Algogator

image: docker:stable

stages:
  - build
  - test

variables:
  IMAGE: ${CI_REGISTRY}/${CI_PROJECT_NAMESPACE}/${CI_PROJECT_NAME}

build:

Algogator

N/W:
Network Know-How
Head first networking

Algogator

It's not something I took into consideration because for the most part the emails were being delivered pretty reliably but I can see why it might be a problem so I just had to look into it since I'm one of the contributors for that project and what I found was that the queues are not reliable (RabbitMQ is a little more reliable) as messages can be lost and redis queue (RQ) does not have a way to automatically re-do it, the failed job is flagged and stored for a year and you will have to write code to handle the exception. 

Algogator

class CNN(nn.Module):
    def __init__(self, vocab_size, embedding_dim, n_filters, filter_sizes, output_dim, 
                 dropout, pad_idx):
        
        super().__init__()
        
        self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx = pad_idx)
        
        self.conv_0 = nn.Parameter(torch.randn((1, 
                                n_filters,