ipkn

// main() from https://github.com/simnalamburt/snippets/blob/master/cpp/y-combinator.cpp  which is licensed under the Apache License 2.0.
// Apache-2.0/MIT. 
// --------

#include <stdint.h>
#include <iostream>

template <typename Func>
auto y(Func f) {
    auto g = [f](auto r){

karpathy

""" 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

EderSantana

Code for Keras plays catch blog post

Train

python qlearn.py

Test

1. Generate figures

shurain

# to generate your dhparam.pem file, run in the terminal
openssl dhparam -out /etc/nginx/ssl/dhparam.pem 2048

balzer82

haje01

텐서플로우 시작하기

글쓴이: 김정주(haje01@gmail.com)

이 문서는 텐서플로우 공식 페이지 내용을 바탕으로 만들어졌습니다.

---

소개

텐서플로우(TensorFlow)는 기계 학습과 딥러닝을 위해 구글에서 만든 오픈소스 라이브러리입니다. 데이터 플로우 그래프(Data Flow Graph) 방식을 사용하였습니다.

nylki

do androids dream of cooking?

The following recipes are sampled from a trained neural net. You can find the repo to train your own neural net here: https://github.com/karpathy/char-rnn Thanks to Andrej Karpathy for the great code! It's really easy to setup.

The recipes I used for training the char-rnn are from a recipe collection called ffts.com And here is the actual zipped data (uncompressed ~35 MB) I used for training. The ZIP is also archived @ archive.org in case the original links becomes invalid in the future.

guswns0528

--- pwn ---

EASY

[DEFCON CTF 2012] PP500
[ksnctf] #23 Villager B [GOT SHELL]

MIDDLE EASY

karpathy

"""
This is a batched LSTM forward and backward pass
"""
import numpy as np
import code

class LSTM:
  
  @staticmethod
  def init(input_size, hidden_size, fancy_forget_bias_init = 3):

takluyver

Copy nbflatten.py to somewhere on $PATH. Then, in the root of a git repository, run these commands:

echo "*.ipynb diff=ipynb" >> .gitattributes 
git config diff.ipynb.textconv nbflatten.py

When you change a notebook and run git diff, you'll see the diff of flattened, simplified notebooks, rather than the full JSON. This does lose some information (metadata, non-text output), but it makes it easier to see simple changes in the notebook.

This doesn't help with merging conflicting changes in notebooks. For that, see nbdiff.org.