<jupyter_start><jupyter_text>Jupyter Notebook 101  Agenda1. Cell types (markdown/code)2. Keyboard input modes (Edit/Command)3. Some useful hotkeys4. Check installed Library5. Install library with pip6. restart kernel / run all  1. Cell types - code cell- markdown cell<jupyter_code>print(1+1)
for i in range(10):
    print(i)<jupyter_output><empty_output><jupyter_text>headline1 headline2 headline3__bold__ _italian_  2. keyboard input modes The Jupyter Notebook has two different keyboard input modes. Edit mode allows you to type code or text into a cell and is indicated by a green cell border. Command mode binds the keyboard to notebook level commands and is indicated by a grey cell border with a blue left margin.- esc => blue (Command mode)- enter => green (Edit mode)  3. Some useful hotkeys  hotkeys- a - insert cell above- b - insert cell below- x - cut selected cell- z - undo cell deletion- h - show keyboard hotkeys (shortcut)- y - change cell to code cell- m - change cell to markdow cell- Shift-Enter - run cell, select below   4. Check environment<jupyter_code>!nvidia-smi 
!pwd
!ls -alh<jupyter_output><empty_output><jupyter_text>4. Check library versions<jupyter_code>import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib
import sklearn
print('tensorflow version: {}'.format(tf.__version__))
print('numpy version: {}'.format(np.__version__))
print('pandas version: {}'.format(pd.__version__))
print('matplotlib version: {}'.format(matplotlib.__version__))
print('scikit learning version: {}'.format(sklearn.__version__))<jupyter_output><empty_output><jupyter_text>5. Import necessary libraries<jupyter_code>!pip install keras
import keras
print('keras version: {}'.format(keras.__version__))
!pip install opencv-python
!apt update && apt install -y libsm6 libxext6
import cv2
print('opencv version: {}'.format(cv2.__version__))
!pip install xmltodict
!pip install -U scikit-learn
!pip install h5py<jupyter_output><empty_output><jupyter_text>6. upload your code 1. ftp your code to twgc-data.nchc.org.tw2. scp the code to working directory<jupyter_code>!cp -rf ./userdata/coslib/ .
!ls<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.09861111111111111, 'fraction_numerical': 0.013888888888888888, 'mean_word_length': 5.41246290801187, 'pattern_counts': {'":': 0, '<': 21, '<?xml version=': 0, '>': 23, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 1}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '5', 'dir': 'jupyter-structured-clean-dedup', 'id': '270806', 'license': 'MIT', 'n_tokens_mistral': 703, 'n_tokens_neox': 681, 'n_words': 239, 'path': 'toturial/201801_Nvidia Training Data/案例分享-Jupyter with TensorFlow 展示解說程式/Initial Setup - Jupyter Notebook 101.ipynb', 'repo_name': 'TW-NCHC/TWGC', 'size': '2160'}