koshian2

import tensorflow as tf
from tensorflow.keras.applications import InceptionV3, VGG16, MobileNet
from tensorflow.keras.layers import GlobalAveragePooling2D, Dense
from tensorflow.keras.models import Model
from tensorflow.keras.callbacks import History, Callback
import tensorflow.keras.backend as K
from tensorflow.contrib.tpu.python.tpu import keras_support

from keras.utils import to_categorical
from keras.datasets import cifar10

t3easy

image: an-image-with-docker-and-docker-compose

variables:
  DOCKER_TLS_VERIFY: "1"
  DOCKER_CERT_PATH: ".docker"

before_script:
- mkdir -p $DOCKER_CERT_PATH
- echo "$DOCKER_CA" > $DOCKER_CERT_PATH/ca.pem
- echo "$DOCKER_CERT" > $DOCKER_CERT_PATH/cert.pem

eiichi-worker

データベースの正規化

正規化の種類

• 非正規形
• 第1～第5正規形
• ボイスコッド正規形

用語

akira345

# Import the SDK
import boto3
import datetime
import os

rds = boto3.client('rds', region_name='us-east-1')

db_instance_identifier = "mysql-db"
log_base_path = "./log/"

dlinsley

import boto3
import argparse
import string

parser = argparse.ArgumentParser('Change storage class of s3 objects')
parser.add_argument('--bucket', dest='myBucket', default='yourBucketName', help='S3 Bucket to search')
parser.add_argument('--from', dest='fromPath', default='', help='s3 path to start search from')

cliArgs = parser.parse_args() 

svmotha

import boto3

class tableCreate(object):
    def __init__(self, **kwargs):
        self.__dict__.update(kwargs)

    # Query client and list_tables to see if table exists or not
    def queryCreate(self):
        # Instantiate your dynamo client object
        client = boto3.client('dynamodb')

t-ae

"""
The MIT License (MIT)

Copyright (c) 2018 Takehiro Araki.

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is

musyoku

import math
import numpy as np

batchsize = 2
r = 2
out_channels = 3
in_channels = r ** 2 * out_channels
in_height = 3
in_width = 3
out_height = in_height * r

telegraphic

""" 
Parse output of nvidia-smi into a python dictionary.
This is very basic!
"""

import subprocess
import pprint

sp = subprocess.Popen(['nvidia-smi', '-q'], stdout=subprocess.PIPE, stderr=subprocess.PIPE)

gcchaan

エンジニア向け研修

• CARTA HOLDINGS（旧VOYAGE GROUP）
  • 技術広報が新卒研修<Open AIハッカソン>をスパイしてみた - (2023/04/11)
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  • 【新卒研修】監修者@t_wadaと読む！プログラマが知るべき97のこと読書会 - (2024/04/09)
• Classi
  • 当たり前にリリースしていく ~ 新卒研修編 - (2021/05/20)
  • リモートワークのための質問力向上研修を実施しました - (2021/12/07)
  • Classi 2025年新卒エンジニア研修「そーだい塾」を開催しました - (2025/06/18)
• CyberZ