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I liked the way Grokking the coding interview organized problems into learnable patterns. However, the course is expensive and the majority of the time the problems are copy-pasted from leetcode. As the explanations on leetcode are usually just as good, the course really boils down to being a glorified curated list of leetcode problems.
So below I made a list of leetcode problems that are as close to grokking problems as possible.
| # 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 |
| """ Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """ | |
| import numpy as np | |
| import cPickle as pickle | |
| import gym | |
| # hyperparameters | |
| H = 200 # number of hidden layer neurons | |
| batch_size = 10 # every how many episodes to do a param update? | |
| learning_rate = 1e-4 | |
| gamma = 0.99 # discount factor for reward |
| """Toy code for | |
| http://stackoverflow.com/questions/2150108/efficient-way-to-shift-a-list-in-python | |
| Example results of timeAll on my system with Python 2.6.4: | |
| {'shiftCopy': 50.579999999999984, | |
| 'shiftExtend': 3.4200000000000017, | |
| 'shiftInPlace': 3.4099999999999966, | |
| 'shifted': 95.409999999999997} |
Using Python's built-in defaultdict we can easily define a tree data structure:
def tree(): return defaultdict(tree)That's it!
| from pylab import * | |
| from scipy.ndimage import measurements | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import time | |
| import sys | |
| import signal | |
| def signal_handler(signal, frame): | |
| sys.exit(0) |
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.
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