nitish11

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

vpekar

#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=================================================================
Selecting dimensionality reduction with Pipeline and GridSearchCV
=================================================================

This example constructs a pipeline that does dimensionality
reduction followed by prediction with a support vector
classifier. It demonstrates the use of GridSearchCV and

pratos

Install Zeppelin in Ubuntu systems

• First install Java, Scala and Spark in Ubuntu

  • Install Java

    sudo apt-add-repository ppa:webupd8team/java
    sudo apt-get update
    sudo apt-get install oracle-java8-installer
    

ajmaradiaga

import numpy as np

def gradient_descent_runner(points, starting_m, starting_b, learning_rate, num_iterations):
	m = starting_m
	b = starting_b
	
	for i in range(num_iterations):
		m, b = step_gradient(m, b, np.array(points), learning_rate)

	return m, b

kangeugine

states = ('Rainy', 'Sunny')
 
observations = ('walk', 'shop', 'clean')
 
start_probability = {'Rainy': 0.6, 'Sunny': 0.4}
 
transition_probability = {
   'Rainy' : {'Rainy': 0.7, 'Sunny': 0.3},
   'Sunny' : {'Rainy': 0.4, 'Sunny': 0.6},
   }

kmclaugh

import pandas as pd
import numpy as np
import seaborn as sns

from keras.layers import Dense
from keras.models import Model, Sequential
from keras import initializers

## ---------- Create our linear dataset ---------------
## Set the mean, standard deviation, and size of the dataset, respectively

santi-pdp

asdf8601

Methods to improve Time series forecast (including ARIMA, Holt's winter)

By: Gurchetan Singh

SOURCE: https://www.analyticsvidhya.com/blog/2018/02/time-series-forecasting-methods/

Introduction

jayspeidell

# Info on how to get your api key (kaggle.json) here: https://github.com/Kaggle/kaggle-api#api-credentials
!pip install kaggle
api_token = {"username":"USERNAME","key":"API_KEY"}
import json
import zipfile
import os
with open('/content/.kaggle/kaggle.json', 'w') as file:
    json.dump(api_token, file)
!chmod 600 /content/.kaggle/kaggle.json
!kaggle config path -p /content

jeremyjordan

from keras.callbacks import Callback
import keras.backend as K
import numpy as np

class SGDRScheduler(Callback):
    '''Cosine annealing learning rate scheduler with periodic restarts.

    # Usage
        ```python
            schedule = SGDRScheduler(min_lr=1e-5,