mayankgrwl97

import torch

input = torch.arange(4*4).view(1, 1, 4, 4).float()
print(input)
'''
tensor([[[[ 0.,  1.,  2.,  3.],
          [ 4.,  5.,  6.,  7.],
          [ 8.,  9., 10., 11.],
          [12., 13., 14., 15.]]]])
'''

mayankgrwl97

# Show Tensor Images utility function

from torchvision.utils import make_grid
import matplotlib.pyplot as plt

def show_tensor_images(image_tensor, num_images=25, size=(1, 28, 28)):
    '''
    Function for visualizing images: Given a tensor of images, number of images, and
    size per image, plots and prints the images in a uniform grid.
    '''

mayankgrwl97

import argparse
import os
import random

import cv2
import numpy as np

###############################################################################

IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG']

mayankgrwl97

import argparse
import glob
import multiprocessing
import os
from functools import partial

import cv2
from tqdm import tqdm

mayankgrwl97

def conv_forward(X, W):
    '''
    The forward computation for a convolution function
    
    Arguments:
    X -- output activations of the previous layer, numpy array of shape (n_H_prev, n_W_prev) assuming input channels = 1
    W -- Weights, numpy array of size (f, f) assuming number of filters = 1
    
    Returns:
    H -- conv output, numpy array of size (n_H, n_W)

mayankgrwl97

def conv_backward(dH, cache):
    '''
    The backward computation for a convolution function
    
    Arguments:
    dH -- gradient of the cost with respect to output of the conv layer (H), numpy array of shape (n_H, n_W) assuming channels = 1
    cache -- cache of values needed for the conv_backward(), output of conv_forward()
    
    Returns:
    dX -- gradient of the cost with respect to input of the conv layer (X), numpy array of shape (n_H_prev, n_W_prev) assuming channels = 1

mayankgrwl97

Studying for a Tech Interview Sucks, so Here's a Cheat Sheet to Help

This list is meant to be a both a quick guide and reference for further research into these topics. It's basically a summary of that comp sci course you never took or forgot about, so there's no way it can cover everything in depth. It also will be available as a gist on Github for everyone to edit and add to.

Data Structure Basics

###Array ####Definition:

• Stores data elements based on an sequential, most commonly 0 based, index.
• Based on tuples from set theory.