emrekgn

# application.yml file of the config server (Spring Cloud Config Server)
server:
  port: 9999 # or whatever
spring:
  application:
    name: config-service
  cloud:
    config:
      server:
        git:

laughinghan

• any: magic, ill-behaved type that acts like a combination of never (the proper [bottom type]) and unknown (the proper [top type])
  • Anything except never is assignable to any, and any is assignable to anything at all.
  • Identities: any & AnyTypeExpression = any, any | AnyTypeExpression = any
  • Key TypeScript feature that allows for [gradual typing].
• unknown: proper, well-behaved [top type]
  • Anything at all is assignable to unknown. unknown is only assignable to itself (unknown) and any.
  • Identities: unknown & AnyTypeExpression = AnyTypeExpression, unknown | AnyTypeExpression = unknown

• Prefer over any whenever possible. Anywhere in well-typed code you're tempted to use any, you probably want unknown.

reddragon

How transferable are features in deep neural networks? - Yosinski et al.

• Transfer Learning

  • Train on a base network, try to take that network and tweak it to work for a new target network.
  • Notes from CS231N.

• Tries to figure out how much information can we transfer between networks trained on different datasets.

• Quantifies the transferability by layer.

• Hypothesis:

• First few layers are general (Gabor Filters kind of features) and can adapt well.

emrekgn

#!/usr/bin/env python

"""

Print (and write to JSON file) system information in a cross-platform manner.

Output contains information about platform, BIOS, CPU, memory, disk, GPU, network, peripheral devices, installed
packages, motherboard and users.

This script heavily relies on psutil and some other bash/powershell commands. See requirements.txt for dependency list.

koaning

import tensorflow as tf
import numpy as np
import uuid

x = tf.placeholder(shape=[None, 3], dtype=tf.float32)
nn = tf.layers.dense(x, 3, activation=tf.nn.sigmoid)
nn = tf.layers.dense(nn, 5, activation=tf.nn.sigmoid)
encoded = tf.layers.dense(nn, 2, activation=tf.nn.sigmoid)
nn = tf.layers.dense(encoded, 5, activation=tf.nn.sigmoid)
nn = tf.layers.dense(nn, 3, activation=tf.nn.sigmoid)

yurishkuro

• What do Etcd, Consul, and Zookeeper do?
  • Service Registration:
    • Host, port number, and sometimes authentication credentials, protocols, versions numbers, and/or environment details.
  • Service Discovery:
    • Ability for client application to query the central registry to learn of service location.
  • Consistent and durable general-purpose K/V store across distributed system.
    • Some solutions support this better than others.
    • Based on Paxos or some derivative (i.e. Raft) algorithm to quickly converge to a consistent state.
• Centralized locking can be based on this K/V store.

siemanko

"""Short and sweet LSTM implementation in Tensorflow.

Motivation:
When Tensorflow was released, adding RNNs was a bit of a hack - it required
building separate graphs for every number of timesteps and was a bit obscure
to use. Since then TF devs added things like `dynamic_rnn`, `scan` and `map_fn`.
Currently the APIs are decent, but all the tutorials that I am aware of are not
making the best use of the new APIs.

Advantages of this implementation:

JonasGroeger

#!/usr/bin/env bash

# Documentation
# https://docs.gitlab.com/ce/api/projects.html#list-projects

NAMESPACE="YOUR_NAMESPACE"
BASE_PATH="https://gitlab.example.com/"
PROJECT_SEARCH_PARAM=""
PROJECT_SELECTION="select(.namespace.name == \"$NAMESPACE\")"
PROJECT_PROJECTION="{ "path": .path, "git": .ssh_url_to_repo }"

rbrick

import javax.crypto.Cipher;
import java.nio.ByteBuffer;
import java.security.GeneralSecurityException;
import java.security.KeyFactory;
import java.security.KeyPair;
import java.security.KeyPairGenerator;
import java.security.NoSuchAlgorithmException;
import java.security.PrivateKey;
import java.security.PublicKey;
import java.security.SecureRandom;

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.