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Hyperparameter are simply 'knobs' and 'turns' used to tune the statistical model of deep learing. 
They are model-specific properties that you fix even before specifying a network and further tune them to create the required fitting of your data.
List of few hyperparameters are:
1. Learning rate
2. Decay rate
3. Number of hidden layers
4. Dropout
5. Activation function
6. Momentum
7. Batch size & a lot other..

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import itertools
import threading
import time
import sys

done = False
#here is the animation
def animate():
    for c in itertools.cycle(['|', '/', '-', '\\']):
        if done:

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from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot
from keras.utils import plot_model
from kt_utils import *


plot_model(happyModel, to_file='HappyModel.png')
SVG(model_to_dot(model).create(prog='dot', format='svg'))

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If You've Never Used Sklearn's Pipeline Constructor...You're Doing It Wrong

How To Use sklearn Pipelines, FeatureUnions, and GridSearchCV With Your Own Transformers

By Emily Gill and Amber Rivera

What's a Pipeline and Why Use One?

The Pipeline constructor from sklearn allows you to chain transformers and estimators together into a sequence that functions as one cohesive unit. For example, if your model involves feature selection, standardization, and then regression, those three steps, each as it's own class, could be encapsulated together via Pipeline.

Benefits: readability, reusability and easier experimentation.

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"""
A demo of creating a new database via SQL Alchemy.

Under MIT License from sprin (https://gist.github.com/sprin/5846464/)

This module takes the form of a nosetest with three steps:
    - Set up the new database.
    - Create a table in the new database.
    - Teardown the new database.
"""

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# !!! RUN THIS CELL ONLY ON GOOGLE COLAB !!!
! wget https://raw.githubusercontent.com/hse-aml/intro-to-dl/master/setup_google_colab.py -O setup_google_colab.py
import setup_google_colab

# run tensorboard in background
import os
os.system("tensorboard --logdir=./logs --host 0.0.0.0 --port 6006 &")

# expose port and show the link
setup_google_colab.expose_port_on_colab(6006)

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!pip install -q "fastai==0.7.0" Pillow==4.1.1 torchtext==0.2.3
!apt-get -qq install -y libsm6 libxext6 && pip install -q -U opencv-python

from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag
platform = '{}{}-{}'.format(get_abbr_impl(), get_impl_ver(), get_abi_tag())
# !apt update -q
!apt install -y libsm6 libxext6

from os import path
accelerator = 'cu80' if path.exists('/opt/bin/nvidia-smi') else 'cpu'

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