USING httpsraw.githubusercontent.comBeeDrHUIntroduction-to-Py.pdf
•
0 likes•2 views
*USING 'https://raw.githubusercontent.com/BeeDrHU/Introduction-to-Python-CSPC-323- /main/sales_forecast.csv' please fix the errors for the RNN using python so the code can run without errors, Thank you* ##Defining Function to Build Multivariate Data Structure def multivariate_data(dataset, target, start_index, end_index, history_size, target_size, step, single_step=False): data = [] labels = [] start_index = start_index + history_size if end_index is None: end_index = len(dataset) - target_size for i in range(start_index, end_index): indices = range(i-history_size, i, step) data.append(dataset[indices]) if single_step: labels.append(target[i+target_size]) else: labels.append(target[i:i+target_size]) return np.array(data), np.array(labels) ##Define the Multi-step Plotting Structure def multi_step_plot(history, true_future, prediction): plt.figure(figsize=(12, 6)) num_in = create_time_steps(len(history)) num_out = len(true_future) plt.plot(num_in, np.array(history[:, 1]), label='History') plt.plot(np.arange(num_out)/STEP, np.array(true_future), 'bo', label='True Future') if prediction.any(): plt.plot(np.arange(num_out)/STEP, np.array(prediction), 'ro', label='Predicted Future') plt.legend(loc='upper left') plt.show() ##Define Function to Plot training and Validation over Time def plot_train_history(history, title): loss = history.history['loss'] val_loss = history.history['val_loss'] epochs = range(len(loss)) plt.figure() plt.plot(epochs, loss, 'b', label='Training loss') plt.plot(epochs, val_loss, 'r', label='Validation loss') plt.title(title) plt.legend() plt.show() ##How far into the past do you want the model to see? past_history= ##How far into the future do you want the model to see? future_target= ## STEP= df_val = len(dataset) - past_history - future_target x_train_multi, y_train_multi = multivariate_data(dataset, dataset[:, 1], 0, df_train, past_history, future_target, STEP) x_val_multi, y_val_multi = multivariate_data(dataset, dataset[:, 1], df_train, df_val, past_history, future_target, STEP) ##Define Constants BATCH_SIZE = BUFFER_SIZE = EPOCHS = EVALUATION_INTERVAL = ##Adding Layers Due to Complexity train_data_multi = tf.data.Dataset.from_tensor_slices(()) train_data_multi = train_data_multi.cache().shuffle().batch().repeat() val_data_multi = tf.data.Dataset.from_tensor_slices(()) val_data_multi = val_data_multi.batch().repeat() multi_step_model = tf.keras.models.Sequential() multi_step_model.add(tf.keras.layers.LSTM()) multi_step_model.add(tf.keras.layers.LSTM()) multi_step_model.add(tf.keras.layers.Dense(72)) multi_step_model.compile(optimizer=tf.keras.optimizers.RMSprop(clipvalue=1.0), loss='mae') ##Train the Model multi_step_history = multi_step_model.fit(train_data_multi, epochs=, steps_per_epoch=, validation_data=, validation_steps=50) ##Predict Future Data for x, y in .take(3): multi_step_plot(x[0], y[0], .predict(x)[0]) #Evaluate the model on the test data test_data_multi = tf.data.Dataset.from_tensor_slices()) test_data_mu.
Report
Share
Report
Share
Download to read offline
![*USING 'https://raw.githubusercontent.com/BeeDrHU/Introduction-to-Python-CSPC-323-
/main/sales_forecast.csv'
please fix the errors for the RNN using python so the code can run without errors, Thank you*
##Defining Function to Build Multivariate Data Structure
def multivariate_data(dataset, target, start_index, end_index, history_size,
target_size, step, single_step=False):
data = []
labels = []
start_index = start_index + history_size
if end_index is None:
end_index = len(dataset) - target_size
for i in range(start_index, end_index):
indices = range(i-history_size, i, step)
data.append(dataset[indices])
if single_step:
labels.append(target[i+target_size])
else:
labels.append(target[i:i+target_size])
return np.array(data), np.array(labels)
##Define the Multi-step Plotting Structure
def multi_step_plot(history, true_future, prediction):
plt.figure(figsize=(12, 6))
num_in = create_time_steps(len(history))
num_out = len(true_future)
plt.plot(num_in, np.array(history[:, 1]), label='History')
plt.plot(np.arange(num_out)/STEP, np.array(true_future), 'bo',
label='True Future')
if prediction.any():](https://image.slidesharecdn.com/usinghttpsraw-230323052842-8ed9bd20/85/USING-httpsraw-githubusercontent-comBeeDrHUIntroduction-to-Py-pdf-1-320.jpg)
![plt.plot(np.arange(num_out)/STEP, np.array(prediction), 'ro',
label='Predicted Future')
plt.legend(loc='upper left')
plt.show()
##Define Function to Plot training and Validation over Time
def plot_train_history(history, title):
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(loss))
plt.figure()
plt.plot(epochs, loss, 'b', label='Training loss')
plt.plot(epochs, val_loss, 'r', label='Validation loss')
plt.title(title)
plt.legend()
plt.show()
##How far into the past do you want the model to see?
past_history=
##How far into the future do you want the model to see?
future_target=
##
STEP=
df_val = len(dataset) - past_history - future_target
x_train_multi, y_train_multi = multivariate_data(dataset, dataset[:, 1], 0,
df_train, past_history,
future_target, STEP)
x_val_multi, y_val_multi = multivariate_data(dataset, dataset[:, 1],
df_train, df_val, past_history,](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
Using the following code Install Packages pip install .pdf
Using the following code:
##Install Packages
!pip install tensorflow
!pip install matplotlib
!pip install numpy
!pip install pandas
##Import Statements
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
##Bringing in our dataset
url= 'https://raw.githubusercontent.com/BeeDrHU/Introduction-to-Python-CSPC-323-
/main/sales_forecast.csv'
data = pd.read_csv(url, sep=',')
##Filtering and Cleaning
data= data[['Store', 'Date', 'Temperature', 'Fuel_Price', 'CPI' , 'Unemployment', 'Weekly_Sales',
'IsHoliday_y']]
data['Date'] = pd.to_datetime(data['Date'], format='%d/%m/%Y')
data = data.set_index('Date')
data = data.sort_index()
##Checklist and Quality Assurance
data.isnull()
print(f'Number of rows with missing values: {data.isnull().any(axis=1).mean()}')
data.info()
##Subsetting variable to predict
df= data['Weekly_Sales']
df.plot()
##Train and Test Split
start_train = datetime(2010, 2, 5)
end_train = datetime(2011, 12, 30)
end_test = datetime(2012, 7, 13)
msk_train = (data.index >= start_train) & (data.index <= end_train)
msk_test = (data.index >= end_train) & (data.index <= end_test)
df_train = df.loc[msk_train]
df_test = df.loc[msk_test]
df_train.plot()
df_test.plot()
##Normalizing our data
uni_data= df.values.astype(float)
df_train= int(len(df_train))
uni_train_mean= uni_data[:df_train].mean()
uni_train_std= uni_data[:df_train].std()
uni_data= (uni_data-uni_train_mean)/uni_train_std
##Build the features dataset to make the model multivariate
features_considered = ['Temperature', 'Fuel_Price', 'CPI']
features = data[features_considered]
features.index = data.index
##Standardizing the Data
dataset = features.values
data_mean = dataset[:df_train].mean(axis=0)
data_std = dataset[:df_train].std(axis=0)
dataset = (dataset-data_mean)/data_std
##Splitting data into training and testing
x_train = dataset[msk_train]
y_train = uni_data[:df_train]
x_test = dataset[msk_test]
y_test = uni_data[df_train:]
##Defining the model architecture
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(64, activation='relu', input_shape=[x_train.shape[1]]),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(1)
])
##Compiling the model
model.compile(optimizer=tf.keras.optimizers.RMSprop(),
loss='mae')
##Fitting the model to the training data
history = model.fit(x_train, y_train,
epochs=100,
batch_size=64,
validation_split=0.2,
verbose=0)
##Evaluating the model on the test data
results = model.evaluate(x_test, y_test, verbose=0)
print(f'Test loss: {results}')
##Making predictions on new data
predictions = model.predict(x_test)
##Plotting the results
fig, ax = plt.subplots(figsize=(10,5))
ax.plot(np.arange(len(y_test)), y_test, label='Actual')
ax.plot(np.arange(len(predictions)), predictions, label='Predicted')
ax.legend()
plt.title('Actual vs Predicted Weekly Sales')
plt.xlabel('Week')
plt.ylabel('Normalized Sales')
plt.show()
*BUILD the RNN*
##Defining Function to Build.
"Optimization of a .NET application- is it simple ! / ?", Yevhen Tatarynov
Optimization of .NET application seems complex and tied full task, but don’t hurry up with conclusions. Let’s look on several cases from real projects.
For this we:
look under the hood of an application from a real project;
define the metric for optimization;
choose the necessary tools;
find bottlenecks /memory leaks and best practice to resolve them.
We'll improve the application step by step and we’ll what with simple analysis and simple best practice we can significantly reduce total resources usage.
Viktor Tsykunov: Azure Machine Learning Service
Viktor Tsykunov: Azure Machine Learning Service
AI & BigData Online Day 2021
Website - http://aiconf.com.ua
Youtube - https://www.youtube.com/startuplviv
FB - https://www.facebook.com/aiconf
Recommended
Using the following code Install Packages pip install .pdf
Using the following code:
##Install Packages
!pip install tensorflow
!pip install matplotlib
!pip install numpy
!pip install pandas
##Import Statements
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
##Bringing in our dataset
url= 'https://raw.githubusercontent.com/BeeDrHU/Introduction-to-Python-CSPC-323-
/main/sales_forecast.csv'
data = pd.read_csv(url, sep=',')
##Filtering and Cleaning
data= data[['Store', 'Date', 'Temperature', 'Fuel_Price', 'CPI' , 'Unemployment', 'Weekly_Sales',
'IsHoliday_y']]
data['Date'] = pd.to_datetime(data['Date'], format='%d/%m/%Y')
data = data.set_index('Date')
data = data.sort_index()
##Checklist and Quality Assurance
data.isnull()
print(f'Number of rows with missing values: {data.isnull().any(axis=1).mean()}')
data.info()
##Subsetting variable to predict
df= data['Weekly_Sales']
df.plot()
##Train and Test Split
start_train = datetime(2010, 2, 5)
end_train = datetime(2011, 12, 30)
end_test = datetime(2012, 7, 13)
msk_train = (data.index >= start_train) & (data.index <= end_train)
msk_test = (data.index >= end_train) & (data.index <= end_test)
df_train = df.loc[msk_train]
df_test = df.loc[msk_test]
df_train.plot()
df_test.plot()
##Normalizing our data
uni_data= df.values.astype(float)
df_train= int(len(df_train))
uni_train_mean= uni_data[:df_train].mean()
uni_train_std= uni_data[:df_train].std()
uni_data= (uni_data-uni_train_mean)/uni_train_std
##Build the features dataset to make the model multivariate
features_considered = ['Temperature', 'Fuel_Price', 'CPI']
features = data[features_considered]
features.index = data.index
##Standardizing the Data
dataset = features.values
data_mean = dataset[:df_train].mean(axis=0)
data_std = dataset[:df_train].std(axis=0)
dataset = (dataset-data_mean)/data_std
##Splitting data into training and testing
x_train = dataset[msk_train]
y_train = uni_data[:df_train]
x_test = dataset[msk_test]
y_test = uni_data[df_train:]
##Defining the model architecture
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(64, activation='relu', input_shape=[x_train.shape[1]]),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(1)
])
##Compiling the model
model.compile(optimizer=tf.keras.optimizers.RMSprop(),
loss='mae')
##Fitting the model to the training data
history = model.fit(x_train, y_train,
epochs=100,
batch_size=64,
validation_split=0.2,
verbose=0)
##Evaluating the model on the test data
results = model.evaluate(x_test, y_test, verbose=0)
print(f'Test loss: {results}')
##Making predictions on new data
predictions = model.predict(x_test)
##Plotting the results
fig, ax = plt.subplots(figsize=(10,5))
ax.plot(np.arange(len(y_test)), y_test, label='Actual')
ax.plot(np.arange(len(predictions)), predictions, label='Predicted')
ax.legend()
plt.title('Actual vs Predicted Weekly Sales')
plt.xlabel('Week')
plt.ylabel('Normalized Sales')
plt.show()
*BUILD the RNN*
##Defining Function to Build.
"Optimization of a .NET application- is it simple ! / ?", Yevhen Tatarynov
Optimization of .NET application seems complex and tied full task, but don’t hurry up with conclusions. Let’s look on several cases from real projects.
For this we:
look under the hood of an application from a real project;
define the metric for optimization;
choose the necessary tools;
find bottlenecks /memory leaks and best practice to resolve them.
We'll improve the application step by step and we’ll what with simple analysis and simple best practice we can significantly reduce total resources usage.
Viktor Tsykunov: Azure Machine Learning Service
Viktor Tsykunov: Azure Machine Learning Service
AI & BigData Online Day 2021
Website - http://aiconf.com.ua
Youtube - https://www.youtube.com/startuplviv
FB - https://www.facebook.com/aiconf
Running Intelligent Applications inside a Database: Deep Learning with Python...
Running Intelligent Applications inside a Database: Deep Learning with Python...Miguel González-Fierro
In this talk we present a new paradigm of computation where the intelligence is computed inside the database. Standard software systems must get the data from the database to execute a routine. If the size of the data is big, there are inefficiencies due to the data movement. Store procedures tried to solve this issue in the past, allowing for computing simple functions inside the database. However, only simple routines can be executed.
To showcase the capabilities of our new system, we created a lung cancer detection algorithm using Microsoft’s Cognitive Toolkit, also known as CNTK. We used transfer learning between ImageNet dataset, which contains natural images, and a lung cancer dataset, which contains scans of horizontal sections of the lung for healthy and sick patients. Specifically, a pretrained Convolutional Neural Network on ImageNet is used on the lung cancer dataset to generate features. Once the features are computed, a boosted tree is applied to predict whether the patient has cancer or not.
All this process is computed inside the database, so the data movement is minimized. We are even able to execute the algorithm using the GPU of the virtual machine that hosts the database. Using a GPU, we can compute the featurization in less than 1h, in contrast to using a CPU, that would take up to 32h. Finally, we set up an API to connect the solution to a web app, where a doctor can analyze the images and get a prediction of a patient. DN 2017 | Multi-Paradigm Data Science - On the many dimensions of Knowledge D...
Gaining insight from data is not as straightforward as we often wish it would be – as diverse as the questions we’re asking are the quality and the quantity of the data we may have at hand. Any attempt to turn data into knowledge thus strongly depends on it dealing with big or not-so-big data, high- or low-dimensional data, exact or fuzzy data, exact or fuzzy questions, and the goal being accurate prediction or understanding. This presentation emphasizes the need for a multi-paradigm data science to tackle all the challenges we are facing today and may be facing in the future. Luckily, solutions are starting to emerge...
Tom Peters, Software Engineer, Ufora at MLconf ATL 2016
Say What You Mean: Scaling Machine Learning Algorithms Directly from Source Code: Scaling machine learning applications is hard. Even with powerful systems like Spark, Tensor Flow, and Theano, the code you write has more to do with getting these systems to work at all than it does with your algorithm itself. But it doesn’t have to be this way!
In this talk, I’ll discuss an alternate approach we’ve taken with Pyfora, an open-source platform for scalable machine learning and data science in Python. I’ll show how it produces efficient, large scale machine learning implementations directly from the source code of single-threaded Python programs. Instead of programming to a complex API, you can simply say what you mean and move on. I’ll show some classes of problem where this approach truly shines, discuss some practical realities of developing the system, and I’ll talk about some future directions for the project.
ML-Ops how to bring your data science to production
This slides is based on Build 2019 session BRK2004 : Breaking the Wall between Data Scientists and App Developers with MLOps
Benchy: Lightweight framework for Performance Benchmarks
Benchy: Lightweight framework for Performance Benchmarks on Python Scripts.
Presented at XXVI Pernambuco Python User Group Meeting at Recife, Pernambuco, Brazil on 06.04.2013
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
Autonomy and ownership are core to working at Stitch Fix, particularly on the Algorithms team. We enable data scientists to deploy and operate their models independently, with minimal need for handoffs or gatekeeping. By writing a simple function and calling out to an intuitive API, data scientists can harness a suite of platform-provided tooling meant to make ML operations easy. In this talk, we will dive into the abstractions the Data Platform team has built to enable this. We will go over the interface data scientists use to specify a model and what that hooks into, including online deployment, batch execution on Spark, and metrics tracking and visualization.
Using an Array include ltstdiohgt include ltmpih.pdf
Using an Array:
#include <stdio.h>
#include <mpi.h>
int main(int argc, char** argv) {
int rank, size;
MPI_Init(&argc, &argv);
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
MPI_Comm_size(MPI_COMM_WORLD, &size);
// Define topology
int topology[5][2] = {{1, 4}, {0, 2, 3}, {1,-1}, {0, 4}, {0, 3}};
// Initialize index and edge arrays
int index[5] = {0};
int edges[5][3] = {{0}};
// Add edges into index array
int i =0;
int j =0;
for (i = 0; i < 5; i++)
{
index[i] = sizeof(topology[i]) / sizeof(int);
for (j = 0; j < index[i]; j++)
{
edges[i][j] = topology[i][j];
}
}
// Display topology for each process
for (i = 0; i < size; i++)
{
if (rank == i)
{
printf("Process %d has %d neighbors: ", i, index[i]);
for (j = 0; j < index[i]; j++)
{
printf("%d ", edges[i][j]);
}
printf("n");
}
MPI_Barrier(MPI_COMM_WORLD);
}
MPI_Finalize();
return 0;
}
MPI Functions:
#include <mpi.h>
#include <stdio.h>
int main(int argc, char** argv)
{
int rank, size;
MPI_Init(&argc, &argv); // Initialize MPI
MPI_Comm_rank(MPI_COMM_WORLD, &rank); // Get the rank of the current process
MPI_Comm_size(MPI_COMM_WORLD, &size); // Get the total number of processes
int nnodes = 5; // Number of nodes in the topology
int nedges = 8; // Number of edges in the topolofy
// Index of the first edge for each node
int index[5] = {2, 5, 6, 8, 9};
int edges[9] = {4, 1, 0, 2, 3, 1, 4, 0, 3}; // List of all edges
MPI_Comm graph_comm; // Create a new communicator for the graph topology
MPI_Graph_create(MPI_COMM_WORLD, nnodes, index, edges, 0, &graph_comm); // Create the
graph topology
int count; // Number of neighbors
int* neighbors; // Neighbor Ranks
MPI_Graph_neighbors_count(graph_comm, rank, &count); // Get the number of neighbors for the
current process
neighbors = (int*) malloc(count * sizeof(int)); // Allocate memory for the array of neighbor ranks
MPI_Graph_neighbors(graph_comm, rank, count, neighbors); // Get the neighbor ranks for the
current process
// Display process and the node in the topologies neighbors
printf("Process %d has %d neighbors:", rank, count);
int i;
for (i = 0; i < count; i++)
{
printf(" %d", neighbors[i]); // Print the neighbor ranks
}
printf("n");
MPI_Finalize();
return 0;
}
ive provided my own code above in case that helps or makes it easier on you guys.
My output isnt quite right ive been at it for a while if someone could fix the output and explain it to
me id be very happy :)
4. Use Graph topology to create following one. Once you create your topology, use one process
(e.g., process 0 ) to display the number of neighbors and its neighbors at each node (i.e.,
process). Use following two methods to check your topology. (20 points) i) Use two arrays, index
and edges, to display number of neighbors and its neighbors for each node. ii) Use two functions
in MPI, "MPI Graph neighbors count" and "MPI Graph neighbors". llinn@scholar-fe06: /470 $
mpirun -n 5 ./hmwk5q4-b Process 0 has 2 neighbors: 41 Process 1 has 3 neighbors: 023 Process
2 has 1 neighbors: 1 Process 3 has 2 neighbors: 40.
Python for R developers and data scientists
This is an introductory talk aimed at data scientists who are well versed with R but would like to work with Python as well. I will cover common workflows in R and how they translate into Python. No Python experience necessary.
Here is my current code- I need help visualizing my data using Python-.docx
Here is my current code, I need help visualizing my data using Python.
###################################################
from matplotlib import pyplot as plt
import pandas as pd
import numpy as np
#find the location of the data file uploaded
from google.colab import drive
drive.mount('/content/drive')
#create the location as a variable
datapath = '/content/helsinki-vantaa (1).txt'
#read the data in
data = pd.read_csv(datapath, delim_whitespace=True,
usecols=["DATE", "USAF", "TEMP_F", "TEMP_C", "REF_TEMP_C", "DIFF_C"],
parse_dates=['DATE'], index_col='DATE')
selection = data[data.index.to_series().between('1988-01-01', '2018-12-31')]
plt_1 = plt.figure(figsize=(6, 3))
###################################################
Here is my goal for the code.
Here is what the source data looks like:
In this assignment we will plot monthly mean temperatures from the Helsinki-Vantaa airport for the past 30 years. Be sure to comment your code during all parts of this assignment. The final output should look like this: helsinki-vantaa (1) - Notepad File Edit View
.
CARTO en 5 Pasos: del Dato a la Toma de Decisiones [CARTO]
En este webinar repasamos - mediante una demostración con el mercado de Real Estate de Los Angeles como ejemplo - cada uno de los cinco pasos que la plataforma de CARTO sigue para una toma de decisiones eficaz basada en los datos.
Watch it now at: https://go.carto.com/carto-pasos-dato-toma-decisiones-recorded
Silicon valleycodecamp2013
A presentation on machine learning, supervised and unsupervised. Examples of usage of machine learning we see everyday.
Una mujer de 36 a�os que se someti� a un trasplante de ri��n hace 2 .pdf
Una mujer de 36 aos que se someti a un trasplante de rin hace 2 meses desarroll fiebre, malestar
general, mialgias y leucopenia. los medicamentos incluyen prednisona y ciclosporina. una
radiografa de trax muestra infiltrados pulmonares intersticiales bilaterales. durante los siguientes
2 das, su estado respiratorio se deteriora y es intubada y ventilada mecnicamente. Se muestra el
tejido obtenido en una biopsia del pulmn. El tratamiento ms adecuado incluye la administracin
de cul de los siguientes frmacos? y por qu
aciclovir
B.ganciclovir
C. interfern-gamma
D. lamivudina.
Una empresa tiene un ciclo operativo de 120 d�as, un per�odo promedi.pdf
Una empresa tiene un ciclo operativo de 120 das, un perodo promedio de cobro de 40 das y un
perodo promedio de pago de 30 das. La antigedad promedio del inventario de la empresa es
________ das.
80
70
90
50
80
70
90
50.
More Related Content
Similar to USING httpsraw.githubusercontent.comBeeDrHUIntroduction-to-Py.pdf
Running Intelligent Applications inside a Database: Deep Learning with Python...
Running Intelligent Applications inside a Database: Deep Learning with Python...Miguel González-Fierro
In this talk we present a new paradigm of computation where the intelligence is computed inside the database. Standard software systems must get the data from the database to execute a routine. If the size of the data is big, there are inefficiencies due to the data movement. Store procedures tried to solve this issue in the past, allowing for computing simple functions inside the database. However, only simple routines can be executed.
To showcase the capabilities of our new system, we created a lung cancer detection algorithm using Microsoft’s Cognitive Toolkit, also known as CNTK. We used transfer learning between ImageNet dataset, which contains natural images, and a lung cancer dataset, which contains scans of horizontal sections of the lung for healthy and sick patients. Specifically, a pretrained Convolutional Neural Network on ImageNet is used on the lung cancer dataset to generate features. Once the features are computed, a boosted tree is applied to predict whether the patient has cancer or not.
All this process is computed inside the database, so the data movement is minimized. We are even able to execute the algorithm using the GPU of the virtual machine that hosts the database. Using a GPU, we can compute the featurization in less than 1h, in contrast to using a CPU, that would take up to 32h. Finally, we set up an API to connect the solution to a web app, where a doctor can analyze the images and get a prediction of a patient. DN 2017 | Multi-Paradigm Data Science - On the many dimensions of Knowledge D...
Gaining insight from data is not as straightforward as we often wish it would be – as diverse as the questions we’re asking are the quality and the quantity of the data we may have at hand. Any attempt to turn data into knowledge thus strongly depends on it dealing with big or not-so-big data, high- or low-dimensional data, exact or fuzzy data, exact or fuzzy questions, and the goal being accurate prediction or understanding. This presentation emphasizes the need for a multi-paradigm data science to tackle all the challenges we are facing today and may be facing in the future. Luckily, solutions are starting to emerge...
Tom Peters, Software Engineer, Ufora at MLconf ATL 2016
Say What You Mean: Scaling Machine Learning Algorithms Directly from Source Code: Scaling machine learning applications is hard. Even with powerful systems like Spark, Tensor Flow, and Theano, the code you write has more to do with getting these systems to work at all than it does with your algorithm itself. But it doesn’t have to be this way!
In this talk, I’ll discuss an alternate approach we’ve taken with Pyfora, an open-source platform for scalable machine learning and data science in Python. I’ll show how it produces efficient, large scale machine learning implementations directly from the source code of single-threaded Python programs. Instead of programming to a complex API, you can simply say what you mean and move on. I’ll show some classes of problem where this approach truly shines, discuss some practical realities of developing the system, and I’ll talk about some future directions for the project.
ML-Ops how to bring your data science to production
This slides is based on Build 2019 session BRK2004 : Breaking the Wall between Data Scientists and App Developers with MLOps
Benchy: Lightweight framework for Performance Benchmarks
Benchy: Lightweight framework for Performance Benchmarks on Python Scripts.
Presented at XXVI Pernambuco Python User Group Meeting at Recife, Pernambuco, Brazil on 06.04.2013
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
Autonomy and ownership are core to working at Stitch Fix, particularly on the Algorithms team. We enable data scientists to deploy and operate their models independently, with minimal need for handoffs or gatekeeping. By writing a simple function and calling out to an intuitive API, data scientists can harness a suite of platform-provided tooling meant to make ML operations easy. In this talk, we will dive into the abstractions the Data Platform team has built to enable this. We will go over the interface data scientists use to specify a model and what that hooks into, including online deployment, batch execution on Spark, and metrics tracking and visualization.
Using an Array include ltstdiohgt include ltmpih.pdf
Using an Array:
#include <stdio.h>
#include <mpi.h>
int main(int argc, char** argv) {
int rank, size;
MPI_Init(&argc, &argv);
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
MPI_Comm_size(MPI_COMM_WORLD, &size);
// Define topology
int topology[5][2] = {{1, 4}, {0, 2, 3}, {1,-1}, {0, 4}, {0, 3}};
// Initialize index and edge arrays
int index[5] = {0};
int edges[5][3] = {{0}};
// Add edges into index array
int i =0;
int j =0;
for (i = 0; i < 5; i++)
{
index[i] = sizeof(topology[i]) / sizeof(int);
for (j = 0; j < index[i]; j++)
{
edges[i][j] = topology[i][j];
}
}
// Display topology for each process
for (i = 0; i < size; i++)
{
if (rank == i)
{
printf("Process %d has %d neighbors: ", i, index[i]);
for (j = 0; j < index[i]; j++)
{
printf("%d ", edges[i][j]);
}
printf("n");
}
MPI_Barrier(MPI_COMM_WORLD);
}
MPI_Finalize();
return 0;
}
MPI Functions:
#include <mpi.h>
#include <stdio.h>
int main(int argc, char** argv)
{
int rank, size;
MPI_Init(&argc, &argv); // Initialize MPI
MPI_Comm_rank(MPI_COMM_WORLD, &rank); // Get the rank of the current process
MPI_Comm_size(MPI_COMM_WORLD, &size); // Get the total number of processes
int nnodes = 5; // Number of nodes in the topology
int nedges = 8; // Number of edges in the topolofy
// Index of the first edge for each node
int index[5] = {2, 5, 6, 8, 9};
int edges[9] = {4, 1, 0, 2, 3, 1, 4, 0, 3}; // List of all edges
MPI_Comm graph_comm; // Create a new communicator for the graph topology
MPI_Graph_create(MPI_COMM_WORLD, nnodes, index, edges, 0, &graph_comm); // Create the
graph topology
int count; // Number of neighbors
int* neighbors; // Neighbor Ranks
MPI_Graph_neighbors_count(graph_comm, rank, &count); // Get the number of neighbors for the
current process
neighbors = (int*) malloc(count * sizeof(int)); // Allocate memory for the array of neighbor ranks
MPI_Graph_neighbors(graph_comm, rank, count, neighbors); // Get the neighbor ranks for the
current process
// Display process and the node in the topologies neighbors
printf("Process %d has %d neighbors:", rank, count);
int i;
for (i = 0; i < count; i++)
{
printf(" %d", neighbors[i]); // Print the neighbor ranks
}
printf("n");
MPI_Finalize();
return 0;
}
ive provided my own code above in case that helps or makes it easier on you guys.
My output isnt quite right ive been at it for a while if someone could fix the output and explain it to
me id be very happy :)
4. Use Graph topology to create following one. Once you create your topology, use one process
(e.g., process 0 ) to display the number of neighbors and its neighbors at each node (i.e.,
process). Use following two methods to check your topology. (20 points) i) Use two arrays, index
and edges, to display number of neighbors and its neighbors for each node. ii) Use two functions
in MPI, "MPI Graph neighbors count" and "MPI Graph neighbors". llinn@scholar-fe06: /470 $
mpirun -n 5 ./hmwk5q4-b Process 0 has 2 neighbors: 41 Process 1 has 3 neighbors: 023 Process
2 has 1 neighbors: 1 Process 3 has 2 neighbors: 40.
Python for R developers and data scientists
This is an introductory talk aimed at data scientists who are well versed with R but would like to work with Python as well. I will cover common workflows in R and how they translate into Python. No Python experience necessary.
Here is my current code- I need help visualizing my data using Python-.docx
Here is my current code, I need help visualizing my data using Python.
###################################################
from matplotlib import pyplot as plt
import pandas as pd
import numpy as np
#find the location of the data file uploaded
from google.colab import drive
drive.mount('/content/drive')
#create the location as a variable
datapath = '/content/helsinki-vantaa (1).txt'
#read the data in
data = pd.read_csv(datapath, delim_whitespace=True,
usecols=["DATE", "USAF", "TEMP_F", "TEMP_C", "REF_TEMP_C", "DIFF_C"],
parse_dates=['DATE'], index_col='DATE')
selection = data[data.index.to_series().between('1988-01-01', '2018-12-31')]
plt_1 = plt.figure(figsize=(6, 3))
###################################################
Here is my goal for the code.
Here is what the source data looks like:
In this assignment we will plot monthly mean temperatures from the Helsinki-Vantaa airport for the past 30 years. Be sure to comment your code during all parts of this assignment. The final output should look like this: helsinki-vantaa (1) - Notepad File Edit View
.
CARTO en 5 Pasos: del Dato a la Toma de Decisiones [CARTO]
En este webinar repasamos - mediante una demostración con el mercado de Real Estate de Los Angeles como ejemplo - cada uno de los cinco pasos que la plataforma de CARTO sigue para una toma de decisiones eficaz basada en los datos.
Watch it now at: https://go.carto.com/carto-pasos-dato-toma-decisiones-recorded
Silicon valleycodecamp2013
A presentation on machine learning, supervised and unsupervised. Examples of usage of machine learning we see everyday.
Similar to USING httpsraw.githubusercontent.comBeeDrHUIntroduction-to-Py.pdf (20)
Running Intelligent Applications inside a Database: Deep Learning with Python...
Running Intelligent Applications inside a Database: Deep Learning with Python...
DN 2017 | Multi-Paradigm Data Science - On the many dimensions of Knowledge D...
DN 2017 | Multi-Paradigm Data Science - On the many dimensions of Knowledge D...
Tom Peters, Software Engineer, Ufora at MLconf ATL 2016
Tom Peters, Software Engineer, Ufora at MLconf ATL 2016
ML-Ops how to bring your data science to production
ML-Ops how to bring your data science to production
Benchy: Lightweight framework for Performance Benchmarks
Benchy: Lightweight framework for Performance Benchmarks
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
Using an Array include ltstdiohgt include ltmpih.pdf
Using an Array include ltstdiohgt include ltmpih.pdf
Here is my current code- I need help visualizing my data using Python-.docx
Here is my current code- I need help visualizing my data using Python-.docx
CARTO en 5 Pasos: del Dato a la Toma de Decisiones [CARTO]
CARTO en 5 Pasos: del Dato a la Toma de Decisiones [CARTO]
More from adonisjpr
Una mujer de 36 a�os que se someti� a un trasplante de ri��n hace 2 .pdf
Una mujer de 36 aos que se someti a un trasplante de rin hace 2 meses desarroll fiebre, malestar
general, mialgias y leucopenia. los medicamentos incluyen prednisona y ciclosporina. una
radiografa de trax muestra infiltrados pulmonares intersticiales bilaterales. durante los siguientes
2 das, su estado respiratorio se deteriora y es intubada y ventilada mecnicamente. Se muestra el
tejido obtenido en una biopsia del pulmn. El tratamiento ms adecuado incluye la administracin
de cul de los siguientes frmacos? y por qu
aciclovir
B.ganciclovir
C. interfern-gamma
D. lamivudina.
Una empresa tiene un ciclo operativo de 120 d�as, un per�odo promedi.pdf
Una empresa tiene un ciclo operativo de 120 das, un perodo promedio de cobro de 40 das y un
perodo promedio de pago de 30 das. La antigedad promedio del inventario de la empresa es
________ das.
80
70
90
50
80
70
90
50.
Una empresa pag� por �ltima vez un dividendo de �3. Si el rendimient.pdf
Una empresa pag por ltima vez un dividendo de 3. Si el rendimiento esperado de su capital se
estima en 12% y sus acciones se cotizan actualmente en la Bolsa de Valores de Londres a 75, cul
debera ser la tasa de crecimiento de dividendos para asegurar que el precio sea consistente con el
precio de una perpetuidad creciente?.
Una cartera eficiente es una combinaci�n de activos que 0 1 pun.pdf
Una cartera eficiente es una combinacin de activos que:
0 / 1 punto
Logra la mayor covarianza posible entre sus activos.
Ofrece una tasa de rendimiento libre de riesgo al minimizar el riesgo de la cartera.
Logra el rendimiento ms alto para un riesgo dado.
Minimiza el riesgo al garantizar que solo permanezca el riesgo diversificable..
Un objetivo a corto plazo esOpci�n multiple A) una meta espec.pdf
Un objetivo a corto plazo es:
Opcin multiple
A) una meta especfica que los gerentes deben lograr en no ms de un ao para alcanzar sus metas a
largo plazo.
B) una accin especfica que usan los gerentes para alcanzar sus metas a largo plazo.
C) una tctica especfica implementada para apoyar el plan estratgico.
d) un componente especfico de la cuenta de resultados presupuestada..
Tyco International, una empresa de fabricaci�n, demand� a su ex dire.pdf
Tyco International, una empresa de fabricacin, demand a su ex director ejecutivo, Dennis
Kozlowski, por haber recibido bonificaciones no autorizadas de las ganancias de la empresa.
Entre sus otras fechoras, Kozlowski otorg bonos especiales no autorizados a s mismo y a ms de
40 empleados de Tyco. En suma, se apropi indebidamente de fondos de la empresa por ms de
$100 millones que no estaba autorizado a recibir. En esta apropiacin indebida se incluy el uso de
fondos de la empresa para gastos personales, como $18 millones para comprar y redecorar su
apartamento en la ciudad de Nueva York y $1 milln para la fiesta de cumpleaos nmero 40 de su
esposa, celebrada en la isla italiana de Cerdea. La extravagante fiesta cont con una escultura de
hielo de la estatua de David orinando vodka Stolichnaya. Con base en estos y otros delitos
corporativos en procedimientos penales relacionados, Kozlowski actualmente cumple entre 8,33
y 25 aos en el Centro Correccional del Estado Medio en Nueva York. Cmo cree que habra
actuado Kozlowski si hubiera considerado la prueba de divulgacin pblica antes de participar en
estas fechoras corporativas? Y si aplicara la prueba de universalizacin? [Tyco Intl, Ltd. contra
Kozlowski, 2005 DNH 68; 2005 Distribucin de EE. UU. LEXIS 6867 (21 de abril de 2005).].
Un informe de an�lisis de estados financieros no incluye A) una dec.pdf
Un informe de anlisis de estados financieros no incluye: A) una declaracin del auditor, B) una
descripcin general del anlisis, C) material probatorio, D) factores clave cualitativos y
cuantitativos, E) Inferencias tales como previsiones.
El anlisis vertical se utiliza para revelar patrones en los datos que cubren dos o ms perodos
sucesivos. Verdadero o falso?
El ndice de flujo de efectivo sobre activos totales en comparacin con el ndice de activos totales
se puede utilizar como indicador de la calidad de las ganancias. Verdadero o falso.
ubat 2015te Hazine 4 78lik 2044 alt aylk bileik vadeye kadar 2,8.pdf
ubat 2015'te Hazine 4 7/8'lik 2044 alt aylk bileik vadeye kadar %2,80 getiri teklif etti. Kuponlarn
alt ayda bir dendiini kabul ederek tahvilin fiyatn hesaplayn. Nominal deerin 1.000 $ olduunu
varsayalm. (Ara hesaplamalar yuvarlamayn. Cevabnz 2 ondalk basamaa yuvarlayn.)
tahvil fiyat.
Two categories of attackers include hacktivists and state actors. .pdf
Two categories of attackers include hacktivists and state actors.
What are the differences between hacktivists and state-sponsored attackers? Write a report
detailing what you were able to find..
Twitter, el popular sitio de microblogs, se ha enfrentado a graves c.pdf
Twitter, el popular sitio de microblogs, se ha enfrentado a graves cadas en el valor de sus
acciones. Esta cada en el valor de las acciones se ha producido a pesar de que la lnea superior de
la compaa creci un 74% y un 111% en los ltimos resultados trimestrales y anuales,
respectivamente. El servicio es gratuito para los usuarios, con ingresos generados principalmente
por la venta de anuncios. Por qu Twitter ha perdido el favor de los inversores? La base de
usuarios de Twitter de 302 millones de usuarios es mucho menor que la base de usuarios de
Facebook de 1.400 millones de usuarios. Instagram ha experimentado un rpido crecimiento,
acercando el tamao de su base de usuarios al de Twitter. Incluso Snapchat est ganando usuarios a
un ritmo mucho ms rpido que Twitter. La tasa de cambio est aumentando en el sentido de que
cada da se lanzan nuevas redes sociales. Por lo tanto, los inversores estn preocupados por las
presiones competitivas. En comparacin, mientras Twitter contina agregando nuevos usuarios, su
ritmo de crecimiento es lento. Esto se debe a la incapacidad de Twitter para hacer una propuesta
de valor clara a los nuevos usuarios. Aunque cientos de millones de usuarios ms se han
registrado en la plataforma de Twitter, una gran cantidad de ellos no se han convertido en
usuarios activos en la plataforma. Para combatir estos desafos, Twitter ha iniciado cambios en las
funciones de su plataforma y en su liderazgo corporativo. Twitter ha introducido nuevas
funciones para los usuarios, incluidas lneas de tiempo instantneas, pginas de inicio sin sesin y
funciones de mensajera mejoradas. Quizs an ms significativo es el cambio de liderazgo en la
parte superior de la organizacin. Jack Dorsey, cofundador y presidente de la junta, reemplazar a
Costolo como director ejecutivo, al menos hasta que se identifique un sucesor ms permanente.
La tabla enumera varias fuerzas que podran incitar a cambios en la organizacin de Twitter.
Para cada elemento, clasifique la fuerza como fuerza interna o externa.
Fuerza
Pronsticos pesimistas de los analistas de la industria
Regulaciones que afectan a la industria, como las reglas de neutralidad de la red
Innovacin que mejora la experiencia del usuario
Twitter experiment ___ cuando reemplaz a su CEO.
a. cambio de organizacion
b. innovacin
C. desarrollo organizativo
Cules de las siguientes son posibles razones por las que Twitter cambi los roles de su liderazgo
ejecutivo? Marque todo lo que corresponda.
Twitter debe lograr una mayor tasa de conversin de usuarios pasivos a activos.
Twitter se enfrenta a una mayor competencia de empresas como Facebook.
Twitter quera un proceso de toma de decisiones ms gil.
La junta directiva de Twitter exigi cambios en la estructura ejecutiva.
Cuando la junta directiva de Twitter decidi nombrar un nuevo director ejecutivo para restaurar la
confianza de los inversores, este cambio se debi principalmente a qu factor?
Una fuerza externa
Una fuerza interna
cambio organico
Mantenimiento.
Turner, Roth y Lowe son socios que comparten ingresos y p�rdidas en .pdf
Turner, Roth y Lowe son socios que comparten ingresos y prdidas en una proporcin de 2:3:5.
Despus de largos desacuerdos entre los socios y varios perodos improductivos, los socios
deciden liquidar la sociedad. Inmediatamente antes de la liquidacin, el balance de la sociedad
muestra activos totales, $164,400; pasivo total, $110,000; Turner, Capital, $5,700; Roth, Capital,
$15,600; y Lowe, Capital, $33,100. Los ingresos en efectivo de la venta de los activos fueron
suficientes para pagar a los acreedores todo menos $44,000. Requerido: a. Calcule la prdida por
la venta de los activos. b. Distribuya la prdida de la parte a a los socios. C. Determinar cunto, en
su caso, cada socio debe contribuir a la sociedad para cubrir cualquier deficiencia de capital
restante..
Uber - Bir Startupn K�kenleri ve lk G�nleriDava M�terileri ele.pdf
Uber - Bir Startup'n Kkenleri ve lk Gnleri
Dava:
Mterileri eletirmek. Ksa deien iiler. Saldrgan dzenleyiciler. Aldatc yetkililer. "Her ne pahasna
olursa olsun kazan" iyeri kltrnde kural ihlali ve acmaszln vurgulanmas. Sadece yedi ylda
balangtan 70 milyar dolarlk bir iletmeye gemek iin gereken bu mu? Yoksa bu nitelendirmeler,
kskan rakiplerin eletirileri yanl m? Uber ya da dnyann nde gelen ulam hizmetlerinden biri olan
Uber Technologies Inc. olarak bilinen dk maliyetli ara arma hizmetinin heyecanl yolculuuna
uzunca bir gz atalm.
Balarken
2008 ylnda Paris'te bir konferansa katlan teknoloji giriimcileri Garrett Camp ve Travis Kalanick,
karl bir gecede taksi bulmann zorluundan ikayet ediyorlard. Bu dei tokutan, bir dmeye
dokunarak talep zerine bir araba aracak yeni teknoloji yaratma fikri geldi. The Upstarts'ta yazar
Brad Stone, en azndan irketin resmi kken efsanesinin bu olduuna dikkat ekiyor. Aslnda bu fikri
ortaya atan Camp, bir sredir arkada Kalanick ile akll telefon tabanl bir ehir arabas (lks araba veya
siyah araba) paylam hizmeti kavramn tartyordu.
Camp, yelerin favori web sitelerini dier insanlarla paylamalarna olanak tanyan keif ve neri
arama motoru StumbleUpon'un kurucu ortayd. Kalanick ("cal-a-nick" olarak telaffuz edilir) ilk
iini 18 yanda kurdu, bu bir SAT-hazrlk zel ders hizmetiydi. Bir dosya paylam ve hizmet ii olan
Scour, onun ikinci i macerasyd, ancak Kalanick'in byk abasna ve srarna ramen baarsz oldu. Bir
sonraki giriimi, bir video ak giriimi olan Red Swoosh'du. 2007'de 23 milyon dolara satld. Mart
2009'da Camp, Calgary'den eski yksek lisans rencileri Oscar Salazar ve Conrad Whelan ile
birlikte Kalanick'in "mega danman" olarak grev yapt yeni ehir arabas fikrinin denenmesinde ba
ekti. . Bunlardan biri, ilk olarak Haziran 2007'de Apple tarafndan piyasaya srlen ve yalnzca akll
telefon endstrisinde devrim yaratmakla kalmayp ayn zamanda genellikle "paylam ekonomisi"
olarak adlandrlan modern ibirliki ekonomiyi yaratmasyla tannan iPhone iin bir mobil
uygulamann prototipiydi.
Mart 2009'da UberCab olarak kurulan irket, mobil uygulama konseptini ilk olarak New York'ta
test etti, ardndan 2011'de San Francisco'da kullanclarn siyah lks bir araba talep etmek iin
iPhone'larnda bir dmeye basmalarna veya bir metin gndermelerine olanak tanyan bir hizmet
olarak resmi olarak lanse edildi. (limuzin), geleneksel bir taksinin fiyatnn sadece 1,5 kat.
Ancak o zamanki taksi irketlerinden farkl olarak, UberCab'in ayrt edici zellii, bir ulam a irketi
olarak adlandrlmasyd; bu irkette yolcular, mobil uygulama veya web sitesi araclyla hizmet talep
edebilir ve srcnn konumunu (GPS araclyla) takip edebilirdi. telefonlarnda) ve arabann tam olarak
ne zaman geleceini biliyorlar.
Birka arabas olmasna ve ok az kiinin bunu bilmesine ramen, giriim byk bir hit oldu. 2011 ylnda,
ehir ve eyalet dzenleyicilerinin srar zerine, irket adn UberCab'den (nk bir ehir arabas veya taksi
firmasnn ruhsat gerekliliklerini karlamad iin) "sper" anlamna gelen Uber'e (Almanca ber
kelimesi.
Trilogy Enterprises Inc. de Austin, Texas, es una empresa de softwar.pdf
Trilogy Enterprises Inc. de Austin, Texas, es una empresa de software de rpido crecimiento y
proporciona soluciones de software a empresas globales gigantes para mejorar las ventas y el
rendimiento. Se enorgullece de su cultura nica y poco ortodoxa. Muchos de sus enfoques de la
prctica comercial son inusuales, pero en el entorno altamente competitivo y cambiante de
Trilogy, parecen funcionar. No existe un cdigo de vestimenta y los empleados establecen sus
propios horarios, a menudo muy extensos. Tienden a socializar juntos (la edad promedio es de 26
aos), tanto en la cocina bien surtida de la oficina como en eventos patrocinados por la empresa y
viajes a lugares como clubes de baile locales y retiros en Las Vegas y Hawi. Se ha desarrollado
una jerga interna y la historia compartida de la firma ha adquirido el estatus de leyenda. La
responsabilidad es pesada y llega temprano, con una actitud de "hazlo ahora" que prescinde de
largos aprendizajes. Los nuevos reclutas reciben unas pocas semanas de capacitacin intensiva,
conocida como "Universidad de Triloga" y los participantes la describen como "ms como un
campo de entrenamiento que una escuela de negocios".
La informacin se entrega como si fuera una manguera contra incendios y se espera que los
nuevos empleados comprometan su experiencia y vitalidad en todo lo que hacen. Jeff Daniel,
director de reclutamiento universitario, admite que la empresa intensa y poco convencional no es
el empleador para todos. Pero definitivamente es un entorno donde las personas apasionadas por
lo que hacen pueden prosperar. La empresa emplea a unas 700 personas tan apasionadas. Los
gerentes de Trilogy saben que el rpido crecimiento que buscan depende de tener un personal de
las mejores personas que puedan encontrar, capacitadas rpidamente y con amplia responsabilidad
y libertad lo antes posible. El CEO Joe Liemandt dice: En una empresa de software, las personas
lo son todo. No puedes construir la prxima gran empresa de software, que es lo que estamos
tratando de hacer aqu, a menos que ests totalmente comprometido con eso. Por supuesto, los
lderes de todas las empresas dicen: "La gente lo es todo". Pero no actan en consecuencia. Trilogy
hace que encontrar a las personas adecuadas (las llama "grandes personas") sea una misin de toda
la empresa. Los reclutadores buscan activamente a las personas ms frescas, si al menos con
experiencia, en el mercado laboral, buscando ferias de carreras universitarias y departamentos de
ciencias de la computacin para personas talentosas con ambicin e instinto empresarial.
Los altos directivos llevan a cabo las primeras rondas de entrevistas y les informan a los
prospectos que se vern presionados para lograr logros pero que sern bien recompensados. Los
empleados llevan a los reclutas y a sus seres queridos a la ciudad cuando vuelan a Austin para la
visita preliminar estndar de 3 das. Un da tpico puede comenzar con entrevistas agotadoras, pero
terminar con ciclismo de montaa, patinaje sob.
Transforming Data - Creating a Data Set Creating a JOIN between two .pdf
Transforming Data - Creating a Data Set Creating a JOIN between two tables is one of several
ways to consolidate data. Which of the following is NOT true about table JOINS in Tableau?
Select one. JOINs effectively create one physical table of combined data. All JOIN types
combine data in the same way. To JOIN two tables in Tableau, you must first double-click on the
primary table, to reveal the physical layer. After performing a JOIN, we should hide one of the
JOIN keys to ensure we do not have duplicate columns..
Using the location quotients(LQ) for year 2020 for 10 provinces anal.pdf
Using the location quotients(LQ) for year 2020 for 10 provinces analyze what province has the
most relational importance in the following sectors. (i)Agriculture (ii)Forestry, fishing, mining,
quarrying, oil, and gas (iii)Manufacturing In this answer can you please mention the names of
the provinces like Manitoba, Alberta and in the answer its mentions like province A and province
B which don't indicate the province clearly instead please provide the names for them..
Using the packet tracer simulator, create a network topology that in.pdf
Using the packet tracer simulator, create a network topology that includes at least one router, one
switch, and several PCs.
4. Assign IP addresses and subnet masks to the devices in the network, making sure to use
appropriate IP addressing scheme.
5. Connect the devices together using the appropriate cables or connections, including
configuring VLANs on the switch.
6. Configure basic router and switch security features such as ACLs, port security, and VLAN
access controls.
7. Save the network diagram as a reference for future use.
8. Describe the process of creating a network diagram that documents the necessary data,
including IP addresses, subnet masks, and connections between devices, as well as the security
configurations implemented.
9. Submit the network diagram along with a brief report that explains the purpose and role of
each device in the network, as well as the security measures implemented and their justification
10. Analyse a sample medium complex network segment and identify common areas for
improvement and potential risks. 3. Using the packet tracer simulator, create a network topology
that includes at least one router, one switch, and several PCs. 4. Assign IP addresses and subnet
masks to the devices in the network, making sure to use appropriate IP addressing scheme. 5.
Connect the devices together using the appropriate cables or connections, including configuring
VLANs on the switch. 6. Configure basic router and switch security features such as ACLs, port
security, and VLAN access controls. 7. Save the network diagram as a reference for future use.
8. Describe the process of creating a network diagram that documents the necessary data,
including IP addresses, subnet masks, and connections between devices, as well as the security
configurations implemented. 9. Submit the network diagram along with a brief report that
explains the purpose and role of each device in the network, as well as the security measures
implemented and their justification 10. Analyse a sample medium complex network segment and
identify common areas for improvement and potential risks..
using silde 2 to answer silde 1 and 3 Step 17 of 40 - Create T Accou.pdf
using silde 2 to answer silde 1 and 3 Step 17 of 40 - Create T Account Listing Students
A Allowance
Step 18 of 40 - General Journal Entries
1415161718YouRTURN:Createjournalentriestorecordtheincreaseordecreaseintheatlowancebased
upontheamounteyouenteredintheTaccount..
Using financial data and information from Yahoo Finance only, calcul.pdf
Using financial data and information from Yahoo Finance only, calculate the intrinsic value (or
calculated price) of Tesla, Inc.
Using the Free Cash Flow Method of Valu.
using IRAC method Joseph was a team manager of an excursion in the O.pdf
using IRAC method Joseph was a team manager of an excursion in the Okavango Delta. Two of
Joseph' s team were attacked by elephants and left, claiming that it has become too dangerous.
With that Joseph promised the other colleague who remained, Karabo, that if she stayed and all
of the excursion work was completed, Karabo would get monies of one of the colleagues who
left without completing work. Karabo went on and agreed, but after the excursion was done, to
Karabo's surprise Joseph refuses to pay the money to Karabo. in 250-350 words ,advice Karabo
whether she is entitled to the extra payment.
Using Java programming language solve the followingSLL CLASS.pdf
Using Java programming language solve the following:
SLL CLASS:
//************************** SLL.java *********************************
// a generic singly linked list class
public class SLL {
private class SLLNode {
private T info;
private SLLNode next;
public SLLNode() {
this(null,null);
}
public SLLNode(T el) {
this(el,null);
}
public SLLNode(T el, SLLNode ptr) {
info = el; next = ptr;
}
}
protected SLLNode head, tail;
public SLL() {
head = tail = null;
}
public boolean isEmpty() {
return head == null;
}
public void addToHead(T el) {
head = new SLLNode(el,head);
if (tail == null)
tail = head;
}
public void addToTail(T el) {
if (!isEmpty()) {
tail.next = new SLLNode(el);
tail = tail.next;
}
else head = tail = new SLLNode(el);
}
public T deleteFromHead() { // delete the head and return its info;
if (isEmpty())
return null;
T el = head.info;
if (head == tail) // if only one node on the list;
head = tail = null;
else head = head.next;
return el;
}
public T deleteFromTail() { // delete the tail and return its info;
if (isEmpty())
return null;
T el = tail.info;
if (head == tail) // if only one node in the list;
head = tail = null;
else { // if more than one node in the list,
SLLNode tmp; // find the predecessor of tail;
for (tmp = head; tmp.next != tail; tmp = tmp.next);
tail = tmp; // the predecessor of tail becomes tail;
tail.next = null;
}
return el;
}
public void delete(T el) { // delete the node with an element el;
if (!isEmpty())
if (head == tail && el.equals(head.info)) // if only one
head = tail = null; // node on the list;
else if (el.equals(head.info)) // if more than one node on the list;
head = head.next; // and el is in the head node;
else { // if more than one node in the list
SLLNode pred, tmp;// and el is in a nonhead node;
for (pred = head, tmp = head.next;
tmp != null && !tmp.info.equals(el);
pred = pred.next, tmp = tmp.next);
if (tmp != null) { // if el was found;
pred.next = tmp.next;
if (tmp == tail) // if el is in the last node;
tail = pred;
}
}
}
@Override
public String toString() {
if(head == null)
return "[ ]";
String str = "[ ";
SLLNode tmp = head;
while(tmp != null){
str += tmp.info + " ";
tmp = tmp.next;
}
return str+"]";
}
public boolean contains(T el) {
if(head == null)
return false;
SLLNode tmp = head;
while(tmp != null){
if(tmp.info.equals(el))
return true;
tmp = tmp.next;
}
return false;
}
public int size(){
if(head == null)
return 0;
int count = 0;
SLLNode p = head;
while(p != null) {
count++;
p = p.next;
}
return count;
}
}
Part I: Programming Use the singly linked list class (Given below) to implement integers of
unlimited size. Each node of the list should store one digit of the integer. You are required to
implement the addition, subtraction and multiplication operations. In addition, provide a test
class that will do the following: It will keep asking the user to enter two integers, choose one of
the operations (addition, subtraction, multiplication) and then display the result. The program
will stop upon e.
More from adonisjpr (20)
Una mujer de 36 a�os que se someti� a un trasplante de ri��n hace 2 .pdf
Una mujer de 36 a�os que se someti� a un trasplante de ri��n hace 2 .pdf
Una empresa tiene un ciclo operativo de 120 d�as, un per�odo promedi.pdf
Una empresa tiene un ciclo operativo de 120 d�as, un per�odo promedi.pdf
Una empresa pag� por �ltima vez un dividendo de �3. Si el rendimient.pdf
Una empresa pag� por �ltima vez un dividendo de �3. Si el rendimient.pdf
Una cartera eficiente es una combinaci�n de activos que 0 1 pun.pdf
Una cartera eficiente es una combinaci�n de activos que 0 1 pun.pdf
Un objetivo a corto plazo esOpci�n multiple A) una meta espec.pdf
Un objetivo a corto plazo esOpci�n multiple A) una meta espec.pdf
Tyco International, una empresa de fabricaci�n, demand� a su ex dire.pdf
Tyco International, una empresa de fabricaci�n, demand� a su ex dire.pdf
Un informe de an�lisis de estados financieros no incluye A) una dec.pdf
Un informe de an�lisis de estados financieros no incluye A) una dec.pdf
ubat 2015te Hazine 4 78lik 2044 alt aylk bileik vadeye kadar 2,8.pdf
ubat 2015te Hazine 4 78lik 2044 alt aylk bileik vadeye kadar 2,8.pdf
Two categories of attackers include hacktivists and state actors. .pdf
Two categories of attackers include hacktivists and state actors. .pdf
Twitter, el popular sitio de microblogs, se ha enfrentado a graves c.pdf
Twitter, el popular sitio de microblogs, se ha enfrentado a graves c.pdf
Turner, Roth y Lowe son socios que comparten ingresos y p�rdidas en .pdf
Turner, Roth y Lowe son socios que comparten ingresos y p�rdidas en .pdf
Uber - Bir Startupn K�kenleri ve lk G�nleriDava M�terileri ele.pdf
Uber - Bir Startupn K�kenleri ve lk G�nleriDava M�terileri ele.pdf
Trilogy Enterprises Inc. de Austin, Texas, es una empresa de softwar.pdf
Trilogy Enterprises Inc. de Austin, Texas, es una empresa de softwar.pdf
Transforming Data - Creating a Data Set Creating a JOIN between two .pdf
Transforming Data - Creating a Data Set Creating a JOIN between two .pdf
Using the location quotients(LQ) for year 2020 for 10 provinces anal.pdf
Using the location quotients(LQ) for year 2020 for 10 provinces anal.pdf
Using the packet tracer simulator, create a network topology that in.pdf
Using the packet tracer simulator, create a network topology that in.pdf
using silde 2 to answer silde 1 and 3 Step 17 of 40 - Create T Accou.pdf
using silde 2 to answer silde 1 and 3 Step 17 of 40 - Create T Accou.pdf
Using financial data and information from Yahoo Finance only, calcul.pdf
Using financial data and information from Yahoo Finance only, calcul.pdf
using IRAC method Joseph was a team manager of an excursion in the O.pdf
using IRAC method Joseph was a team manager of an excursion in the O.pdf
Using Java programming language solve the followingSLL CLASS.pdf
Using Java programming language solve the followingSLL CLASS.pdf
Recently uploaded
Acetabularia Information For Class 9 .docx
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
The Challenger.pdf DNHS Official Publication
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Overview on Edible Vaccine: Pros & Cons with Mechanism
This ppt include the description of the edible vaccine i.e. a new concept over the traditional vaccine administered by injection.
Operation Blue Star - Saka Neela Tara
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
A Strategic Approach: GenAI in Education
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
BÀI TẬP BỔ TRỢ TIẾNG ANH GLOBAL SUCCESS LỚP 3 - CẢ NĂM (CÓ FILE NGHE VÀ ĐÁP Á...
BÀI TẬP BỔ TRỢ TIẾNG ANH GLOBAL SUCCESS LỚP 3 - CẢ NĂM (CÓ FILE NGHE VÀ ĐÁP Á...Nguyen Thanh Tu Collection
https://app.box.com/s/hqnndn05v4q5a4k4jd597rkdbda0fniiHome assignment II on Spectroscopy 2024 Answers.pdf
Answers to Home assignment on UV-Visible spectroscopy: Calculation of wavelength of UV-Visible absorption
Supporting (UKRI) OA monographs at Salford.pptx
How libraries can support authors with open access requirements for UKRI funded books
Wednesday 22 May 2024, 14:00-15:00.
Unit 8 - Information and Communication Technology (Paper I).pdf
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Unit 2- Research Aptitude (UGC NET Paper I).pdf
This slide describes the research aptitude of unit 2 in the UGC NET paper I.
2024.06.01 Introducing a competency framework for languag learning materials ...
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Normal Labour/ Stages of Labour/ Mechanism of Labour
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
The approach at University of Liverpool.pptx
How libraries can support authors with open access requirements for UKRI funded books
Wednesday 22 May 2024, 14:00-15:00.
special B.ed 2nd year old paper_20240531.pdf
Instagram:-
https://instagram.com/special_education_needs_01?igshid=YmMyMTA2M2Y=
WhatsApp:-
https://chat.whatsapp.com/JVakNIYlSV94x7bwunO3Dc
YouTube:-
https://youtube.com/@special_education_needs
Teligram :- https://t.me/special_education_needs
Slide Shere :-
https://www.slideshare.net/shabnambano20?utm_campaign=profiletracking&utm_medium=sssite&utm_source=ssslideview
Embracing GenAI - A Strategic Imperative
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Mule 4.6 & Java 17 Upgrade | MuleSoft Mysore Meetup #46
Mule 4.6 & Java 17 Upgrade | MuleSoft Mysore Meetup #46
Event Link:-
https://meetups.mulesoft.com/events/details/mulesoft-mysore-presents-exploring-gemini-ai-and-integration-with-mulesoft/
Agenda
● Java 17 Upgrade Overview
● Why and by when do customers need to upgrade to Java 17?
● Is there any immediate impact to upgrading to Mule Runtime 4.6 and beyond?
● Which MuleSoft products are in scope?
For Upcoming Meetups Join Mysore Meetup Group - https://meetups.mulesoft.com/mysore/
YouTube:- youtube.com/@mulesoftmysore
Mysore WhatsApp group:- https://chat.whatsapp.com/EhqtHtCC75vCAX7gaO842N
Speaker:-
Shubham Chaurasia - https://www.linkedin.com/in/shubhamchaurasia1/
Priya Shaw - https://www.linkedin.com/in/priya-shaw
Organizers:-
Shubham Chaurasia - https://www.linkedin.com/in/shubhamchaurasia1/
Giridhar Meka - https://www.linkedin.com/in/giridharmeka
Priya Shaw - https://www.linkedin.com/in/priya-shaw
Shyam Raj Prasad-
https://www.linkedin.com/in/shyam-raj-prasad/
Recently uploaded (20)
Overview on Edible Vaccine: Pros & Cons with Mechanism
Overview on Edible Vaccine: Pros & Cons with Mechanism
BÀI TẬP BỔ TRỢ TIẾNG ANH GLOBAL SUCCESS LỚP 3 - CẢ NĂM (CÓ FILE NGHE VÀ ĐÁP Á...
BÀI TẬP BỔ TRỢ TIẾNG ANH GLOBAL SUCCESS LỚP 3 - CẢ NĂM (CÓ FILE NGHE VÀ ĐÁP Á...
Home assignment II on Spectroscopy 2024 Answers.pdf
Home assignment II on Spectroscopy 2024 Answers.pdf
Unit 8 - Information and Communication Technology (Paper I).pdf
Unit 8 - Information and Communication Technology (Paper I).pdf
2024.06.01 Introducing a competency framework for languag learning materials ...
2024.06.01 Introducing a competency framework for languag learning materials ...
Normal Labour/ Stages of Labour/ Mechanism of Labour
Normal Labour/ Stages of Labour/ Mechanism of Labour
Mule 4.6 & Java 17 Upgrade | MuleSoft Mysore Meetup #46
Mule 4.6 & Java 17 Upgrade | MuleSoft Mysore Meetup #46
USING httpsraw.githubusercontent.comBeeDrHUIntroduction-to-Py.pdf
- 1. *USING 'https://raw.githubusercontent.com/BeeDrHU/Introduction-to-Python-CSPC-323- /main/sales_forecast.csv' please fix the errors for the RNN using python so the code can run without errors, Thank you* ##Defining Function to Build Multivariate Data Structure def multivariate_data(dataset, target, start_index, end_index, history_size, target_size, step, single_step=False): data = [] labels = [] start_index = start_index + history_size if end_index is None: end_index = len(dataset) - target_size for i in range(start_index, end_index): indices = range(i-history_size, i, step) data.append(dataset[indices]) if single_step: labels.append(target[i+target_size]) else: labels.append(target[i:i+target_size]) return np.array(data), np.array(labels) ##Define the Multi-step Plotting Structure def multi_step_plot(history, true_future, prediction): plt.figure(figsize=(12, 6)) num_in = create_time_steps(len(history)) num_out = len(true_future) plt.plot(num_in, np.array(history[:, 1]), label='History') plt.plot(np.arange(num_out)/STEP, np.array(true_future), 'bo', label='True Future') if prediction.any():
- 2. plt.plot(np.arange(num_out)/STEP, np.array(prediction), 'ro', label='Predicted Future') plt.legend(loc='upper left') plt.show() ##Define Function to Plot training and Validation over Time def plot_train_history(history, title): loss = history.history['loss'] val_loss = history.history['val_loss'] epochs = range(len(loss)) plt.figure() plt.plot(epochs, loss, 'b', label='Training loss') plt.plot(epochs, val_loss, 'r', label='Validation loss') plt.title(title) plt.legend() plt.show() ##How far into the past do you want the model to see? past_history= ##How far into the future do you want the model to see? future_target= ## STEP= df_val = len(dataset) - past_history - future_target x_train_multi, y_train_multi = multivariate_data(dataset, dataset[:, 1], 0, df_train, past_history, future_target, STEP) x_val_multi, y_val_multi = multivariate_data(dataset, dataset[:, 1], df_train, df_val, past_history,
- 3. future_target, STEP) ##Define Constants BATCH_SIZE = BUFFER_SIZE = EPOCHS = EVALUATION_INTERVAL = ##Adding Layers Due to Complexity train_data_multi = tf.data.Dataset.from_tensor_slices(()) train_data_multi = train_data_multi.cache().shuffle().batch().repeat() val_data_multi = tf.data.Dataset.from_tensor_slices(()) val_data_multi = val_data_multi.batch().repeat() multi_step_model = tf.keras.models.Sequential() multi_step_model.add(tf.keras.layers.LSTM()) multi_step_model.add(tf.keras.layers.LSTM()) multi_step_model.add(tf.keras.layers.Dense(72)) multi_step_model.compile(optimizer=tf.keras.optimizers.RMSprop(clipvalue=1.0), loss='mae') ##Train the Model multi_step_history = multi_step_model.fit(train_data_multi, epochs=, steps_per_epoch=, validation_data=, validation_steps=50) ##Predict Future Data for x, y in .take(3): multi_step_plot(x[0], y[0], .predict(x)[0]) #Evaluate the model on the test data test_data_multi = tf.data.Dataset.from_tensor_slices()) test_data_multi = test_data_multi.batch() # Define the number of steps in the test dataset steps = len()
- 4. mae_values = [] for x, y in : x_val, y_val= x[:, :-1], y[:,-1] #Make predictions using the model = .predict(x_val, steps=steps) #Compute MAE mae = tf.keras.metrics.mean_absolute_error().numpy() mae_values.append(mae) overall_mae = np.mean(mae_values) print('Overall MAE:', overall_mae)