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# Zipline Pipeline
### Introduction
On any given trading day, the entire universe of stocks consists of thousands of securities. Usually, you will not be interested in investing in all the stocks in the entire universe, but rather, you will likely select only a subset of these to invest. For example, you may only wa... | github_jupyter |
**0. Code for Colab Debugging**
```
from google.colab import drive
drive.mount('/content/gdrive')
%cd /content/gdrive/My Drive/lxmert/src/
!pip install transformers
import torch
print(torch.cuda.is_available())
```
**1. Import pckgs & Set basic configs**
```
# Base packages
import logging
import math
import os
from ... | github_jupyter |
```
%load_ext cypher
import json
import random
geotweets = %cypher match (n:tweet) where n.coordinates is not null return n.tid, n.lang, n.country, n.name, n.coordinates, n.created_at
geotweets = geotweets.get_dataframe()
geotweets.head()
json.loads(geotweets.ix[1]["n.coordinates"])[0][0]
def get_random_coords(df):
... | github_jupyter |
# Network waterfall generation
```
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
from math import sqrt
import re, bisect
from colorama import Fore
```
## Select input file and experiment ID (~10 experiments per file)
- ./startup : Application startu... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score, mean_squared_error
import seaborn as sns
import statsmodels.api as sm
%matplotlib inline
```
# 1. BUSIN... | github_jupyter |
# Taller evaluable sobre la extracción, transformación y visualización de datos usando IPython
**Juan David Velásquez Henao**
jdvelasq@unal.edu.co
Universidad Nacional de Colombia, Sede Medellín
Facultad de Minas
Medellín, Colombia
# Instrucciones
En la carpeta 'Taller' del repositorio 'ETVL-IPython' se enc... | github_jupyter |
## Advanced usage
### Using config files
Instead of specifying all inputs using [set_input](https://inbo.github.io/niche_vlaanderen/lowlevel.html#niche_vlaanderen.Niche.set_input), it is possible to use a config file. A config file can be loaded using [read_config_file](https://inbo.github.io/niche_vlaanderen/lowlevel... | github_jupyter |
### Requirements
#### Jupyter Nbextensions
- Python Markdown
- Load Tex Macros
#### Python & Libs
- Python version $\geq$ 3.4
- Numpy version $\geq$ 1.17
- Pandas version $\geq$ 1.0.3
```
import string
import operator
import functools
import numpy as np
import pandas as pd
from collections import Counter
from I... | github_jupyter |
## Figure tracer transport
```
#import gsw as sw # Gibbs seawater package
import cmocean as cmo
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import matplotlib.gridspec as gspec
%matplotlib inline
from netCDF4 import Dataset
import numpy as np
import pandas as pd
import seaborn as sns
import sys
... | github_jupyter |
# Documentation
- Generate the datasets used for evotuning the esm model
- for each dataset, filter out those sequence longer than 1024
- pfamA_balanced: 18000 entries for 4 clans related to motors
- motor_toolkit: motor toolkit
- kinesin_labelled: kinesin labelled dataset
- pfamA_target_shuffled: pfamA_target
- pfamA_... | github_jupyter |
## Compare Network Architectures
Now that we have a reasonable baseline for our EasyDeepFakes dataset, let's try to improve performance. For starters, let's just compare how a variety of networks perform on this dataset. We will try:
- ResNet
- XResNet
- EfficientNet
- MesoNet
- XceptionNet
```
from fastai.core... | github_jupyter |
# 开发 AI 应用
未来，AI 算法在日常生活中的应用将越来越广泛。例如，你可能想要在智能手机应用中包含图像分类器。为此，在整个应用架构中，你将使用一个用成百上千个图像训练过的深度学习模型。未来的软件开发很大一部分将是使用这些模型作为应用的常用部分。
在此项目中，你将训练一个图像分类器来识别不同的花卉品种。可以想象有这么一款手机应用，当你对着花卉拍摄时，它能够告诉你这朵花的名称。在实际操作中，你会训练此分类器，然后导出它以用在你的应用中。我们将使用[此数据集](http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html)，其中包含 102 个花卉类别。你可以在下面查看... | github_jupyter |
# Machine Learning for Telecom with Naive Bayes
# Introduction
Machine Learning for CallDisconnectReason is a notebook which demonstrates exploration of dataset and CallDisconnectReason classification with Spark ml Naive Bayes Algorithm.
```
from pyspark.sql.types import *
from pyspark.sql import SparkSession
from s... | github_jupyter |
# Feature Engineering with PySpark
## Exploratory Data Analysis
```
import pyspark as sp
sp.version
import sys
print(sys.version_info)
sys.version
```
import os
os.environ["JAVA_HOME"] = "/Library/Java/JavaVirtualMachines/jdk1.8.0_151.jdk/Contents/Home"
```
sc = sp.SparkContext.getOrCreate()
sc.version
# spark sess... | github_jupyter |
# Recommending products with RetailRocket event logs
This IPython notebook illustrates the usage of the [ctpfrec](https://github.com/david-cortes/ctpfrec/) Python package for _Collaborative Topic Poisson Factorization_ in recommender systems based on sparse count data using the [RetailRocket](https://www.kaggle.com/re... | github_jupyter |
```
import numpy as np
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams['axes.titlesize'] = 20
plt.rcParams['axes.titleweight'] = 10
```
## 1. Dataset Read
```
df = pd.read_csv("haberman.csv")
df.head()
```
## 2. Basic Analysis
```
print("No. of features are in given dataset :... | github_jupyter |
```
"""
You can run either this notebook locally (if you have all the dependencies and a GPU) or on Google Colab.
Instructions for setting up Colab are as follows:
1. Open a new Python 3 notebook.
2. Import this notebook from GitHub (File -> Upload Notebook -> "GITHUB" tab -> copy/paste GitHub URL)
3. Connect to an in... | github_jupyter |
```
from os import fsdecode
import subprocess
import math
import json
from numpy import linalg as la, ma
import numpy as np
import time
import os
import julian
import matplotlib.pyplot as plt
import pandas as pd
from numpy.linalg import linalg
from scipy.spatial.transform import Rotation as R
from scipy.spatial import ... | github_jupyter |
# Continuous Control
---
In this notebook, you will learn how to use the Unity ML-Agents environment for the second project of the [Deep Reinforcement Learning Nanodegree](https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893) program.
### 1. Start the Environment
We begin by importing the ne... | github_jupyter |
<a href="https://colab.research.google.com/github/GMTAccount/Projets-scolaires/blob/main/Projet_Gomoku.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
<h1>
Liste des erreurs
</h1>
<ul>
<li style="color:green;">
Recentrer le premier point (fa... | github_jupyter |
# Introduction to Python part IV (And a discussion of linear transformations)
## Activity 1: Discussion of linear transformations
* Orthogonality also plays a key role in understanding linear transformations. How can we understand linear transformations in terms of a composition of rotations and diagonal matrices? ... | github_jupyter |
View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/
My Youtube Channel: https://www.youtube.com/user/MorvanZhou
Dependencies:
* torch: 0.1.11
* torchvision
* matplotlib
```
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.utils.data as Data
import torchvision
... | github_jupyter |
```
import torch
torch.cuda.is_available() # check if GPU is available
```
# Automatic differentiation using Autograd
```
x = torch.ones(2, 2, requires_grad=True)
print(x)
y = x + 2
print(y)
z = y * y * 3
out = z.mean()
print(z, out)
a = torch.randn(2, 2)
a = ((a * 3) / (a - 1))
print(a.requires_grad)
a.requires_gra... | github_jupyter |
# **Welcome to the Python Workshop! Here is your starter code**
```
%matplotlib inline
```
### Try out hello world below! It's easy I promise.
```
print("Hello World")
```
## What are we analyzing?
We're going to be looking at the sales numbers over a 5 year period of batmobiles for Wayne Enterprises. We can see ... | github_jupyter |
```
import cv2
import matplotlib.pyplot as plt
import time
import cProfile
import numpy as np
```
# Walker detection with openCV
## Open video and get video info
```
video_capture = cv2.VideoCapture('resources/TestWalker.mp4')
# From https://www.learnopencv.com/how-to-find-frame-rate-or-frames-per-second-fps-in-open... | github_jupyter |
# 人脸检测
人脸检测，顾名思义，从图像中找到人脸。这是计算机视觉中一个非常经典的物体检测问题。经典人脸检测算法如Viola-Jones算法已经内置在OpenCV中，一度是使用OpenCV实现人脸检测的默认方案。不过OpenCV最新发布的4.5.4版本中提供了一个全新的基于神经网络的人脸检测器。这篇笔记展示了该检测器的使用方法。
## 准备工作
首先载入必要的包，并检查OpenCV版本。
如果你还没有安装OpenCV，可以通过如下命令安装：
```bash
pip install opencv-python
```
```
import cv2
from PIL import Image
print(f"你需要Open... | github_jupyter |
```
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file... | github_jupyter |
<a href="https://colab.research.google.com/github/Eurus-Holmes/PyTorch-Tutorials/blob/master/Training_a__Classifier.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
%matplotlib inline
```
Training a Classifier
=====================
This is it.... | github_jupyter |
# Direct Outcome Prediction Model
Also known as standardization
```
%matplotlib inline
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import GradientBoostingRegressor
from causallib.datasets import load_smoking_weight
from causallib.estimation import Standardization, StratifiedStandardization... | github_jupyter |
Getting rid of bottom bands - Jessica's run (run01)
===================================================
Run01 Jessica's runs (360x360x90, her bathymetry and stratification initial files)
--------------------------------------------------------------
Initial stratifications, Depths 162, 315, 705 m, Across-shelf slice ... | github_jupyter |
# Classifying Fashion-MNIST
Now it's your turn to build and train a neural network. You'll be using the [Fashion-MNIST dataset](https://github.com/zalandoresearch/fashion-mnist), a drop-in replacement for the MNIST dataset. MNIST is actually quite trivial with neural networks where you can easily achieve better than 9... | github_jupyter |
<a href="https://colab.research.google.com/github/HenrryCordovillo/Redes_Neuronales_con_Python/blob/main/Ejercicio_3%2C_Perceptr%C3%B3n.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import numpy as np
from keras.models import Sequential
from... | github_jupyter |
## Observations and Insights
## Dependencies and starter code
```
# Dependencies and Setup
import matplotlib.pyplot as plt
import pandas as pd
import scipy.stats as st
import numpy as np
# Study data files
mouse_metadata = "data/Mouse_metadata.csv"
study_results = "data/Study_results.csv"
# Read the mouse data and ... | github_jupyter |
# Accessing data in a DataSet
After a measurement is completed all the acquired data and metadata around it is accessible via a `DataSet` object. This notebook presents the useful methods and properties of the `DataSet` object which enable convenient access to the data, parameters information, and more. For general ov... | github_jupyter |
# Inverse Analysis of Turbidites by Machine Learning Technique
# Preprocessing of training and test data sets
```
import numpy as np
import os
import ipdb
def connect_dataset(dist_start, dist_end, file_list, outputdir,
topodx=5, offset=5000,gclass_num=4,test_data_num=100):
"""
Connect mul... | github_jupyter |
Plotting with matplotlib - 1
========================
```
# plotting imports
import matplotlib.pyplot as plt
import seaborn as sns
# other imports
import numpy as np
import pandas as pd
from scipy import stats
```
Hello world
---
Using the `pyplot` notation, very similar to how MATLAB works
```
plt.plot([0, 1, 2, 3... | github_jupyter |
## Interacting with CerebralCortex Data
Cerebral Cortex is MD2K's big data cloud tool designed to support population-scale data analysis, visualization, model development, and intervention design for mobile-sensor data. It provides the ability to do machine learning model development on population scale datasets and p... | github_jupyter |
```
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from tqdm import tqdm
%matplotlib inline
from torch.utils.data import Dataset, DataLoader
import torch
import torchvision
import torch.nn as nn
import torch.optim as optim
from torch.nn import functional as F
device = torch.device("cuda" i... | github_jupyter |
# Project: Wrangling and Analyze Data
```
import pandas as pd
import numpy as np
from twython import Twython
import requests
import json
import time
import matplotlib.pyplot as plt
import seaborn as sns
from wordcloud import WordCloud, STOPWORDS
from PIL import Image
import urllib
```
## Data Gathering
In the cells b... | github_jupyter |
```
import sites_positionWithinProteins as pos
reload(pos)
fn_fasta = r"/Volumes/Speedy/FASTA/HUMAN20150706.fasta"
fn_evidence = r"/Users/dblyon/CloudStation/CPR/BTW_sites/sites_positionsWithinProteins_input_v2.txt"
fn_fasta
fa = pos.Fasta()
fa.set_file(fn_fasta)
fa.parse_fasta()
COLUMN_MODSEQ = "Modified sequence"
COL... | github_jupyter |
# Predicting Student Admissions with Neural Networks
In this notebook, we predict student admissions to graduate school at UCLA based on three pieces of data:
- GRE Scores (Test)
- GPA Scores (Grades)
- Class rank (1-4)
The dataset originally came from here: http://www.ats.ucla.edu/
## Loading the data
To load the da... | github_jupyter |
```
import pandas as pd
auto_df = pd.read_csv("automatability.csv") #to transpose
relative_emp_df = pd.read_csv("relativeEmployment.csv") #to transpose
similar_df = pd.read_csv("newsimilarity.csv")
wagechange_df = pd.read_csv("wageChange.csv")
# all_csvs = [auto_df, relative_emp_df, similar_df, wagechange_df]
# for cs... | github_jupyter |
```
%reload_ext autoreload
%autoreload 2
from fastai.basics import *
from pathlib import Path
import pandas as pd
```
# Rossmann
## Data preparation / Feature engineering
Set `PATH` to the path `~/data/rossmann/`. Create a list of table names, with one entry for each CSV that you'll be loading:
- train
- store
- st... | github_jupyter |
```
from fastai.vision.all import *
from moving_mnist.models.conv_rnn import *
from moving_mnist.data import *
if torch.cuda.is_available():
torch.cuda.set_device(1)
print(torch.cuda.get_device_name())
```
# Train Example:
We wil predict:
- `n_in`: 5 images
- `n_out`: 5 images
- `n_obj`: up to 3 objects
``... | github_jupyter |
<a href="https://colab.research.google.com/github/yunjung-lee/class_python_data/blob/master/skin_cancer.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
########## skin cancer in kaggle dataset
#### used knn ,dropout,save file
```
!git clone https... | github_jupyter |
<a href="https://colab.research.google.com/github/will-cotton4/DS-Unit-2-Sprint-3-Classification-Validation/blob/master/module4-rf-gb/LS_DS_234_Random_Forests_Gradient_Boosting.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
_Lambda School Data Scie... | github_jupyter |
# Basic Matplotlib cookbook
By [Terence Parr](https://parrt.cs.usfca.edu). If you like visualization in machine learning, check out my stuff at [explained.ai](https://explained.ai).
This notebook shows you how to generate basic versions of the common plots you'll need.
```
import numpy as np
import pandas as pd
impo... | github_jupyter |
# Parameterization for sediment released by sea-ice
```
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from mpl_toolkits.basemap import Basemap, cm
import netCDF4 as nc
import datetime as dt
import pickle
import scipy.ndimage as ndimage
import xarray as xr
%matplotlib inline
```
##### Parameter... | github_jupyter |
# Enable application insights and add custom logs in your endpoint
## Get your Azure ML Workspace
```
!pip install azureml-core
import azureml
from azureml.core import Workspace
import mlflow.azureml
workspace_name = '<YOUR-WORKSPACE>'
resource_group = '<YOUR-RESOURCE-GROUP>'
subscription_id = '<YOUR-SUBSCRIPTION-ID... | github_jupyter |
# Talks markdown generator for academicpages
Takes a TSV of talks with metadata and converts them for use with [academicpages.github.io](academicpages.github.io). This is an interactive Jupyter notebook ([see more info here](http://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/what_is_jupyter.html)). The co... | github_jupyter |
**Tools - pandas**
*The `pandas` library provides high-performance, easy-to-use data structures and data analysis tools. The main data structure is the `DataFrame`, which you can think of as an in-memory 2D table (like a spreadsheet, with column names and row labels). Many features available in Excel are available pro... | github_jupyter |
```
import os
import sys
from PIL import Image
import numpy as np
import shutil
sys.path.extend(['..'])
from utils.config import process_config
import tensorflow as tf
from tensorflow.layers import (conv2d, max_pooling2d, average_pooling2d, batch_normalization, dropout, dense)
from tensorflow.nn import (relu, softma... | github_jupyter |
# Some useful functions
```
import time
from sklearn.metrics import confusion_matrix
from sklearn.utils.multiclass import unique_labels
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn import svm
from keras.models import Sequential
from keras.layers import Conv2D,Ma... | github_jupyter |
```
import gensim.downloader as api
import gensim
from gensim.models import Phrases
from gensim.models import KeyedVectors, Word2Vec
import numpy as np
import nltk
from nltk.corpus import stopwords
import string
from sklearn.metrics.pairwise import cosine_similarity
import networkx as nx
import ast
import json
filename... | github_jupyter |
```
from __future__ import print_function
import keras
from keras.models import Sequential, Model, load_model
import keras.backend as K
import tensorflow as tf
import pandas as pd
import os
import pickle
import numpy as np
import scipy.sparse as sp
import scipy.io as spio
import isolearn.io as isoio
from scipy.st... | github_jupyter |
# Applied Machine Learning
## Table of contents
* [1. Notebook General Info](#1.-Notebook-General-Info)
* [2. Python Basics](#2.-Python-Basics)
* [2.1 Basic Types](#2.1-Basic-Types)
* [2.2 Lists and Tuples](#2.2-Lists-and-Tuples)
* [2.3 Dictionaries](#2.3-Dictionaries)
* [2.4 Conditions](#2.4-Condition... | github_jupyter |
***
***
# Introduction to Gradient Descent
The Idea Behind Gradient Descent 梯度下降
***
***
<img src='./img/stats/gradient_descent.gif' align = "middle" width = '400px'>
<img align="left" style="padding-right:10px;" width ="400px" src="./img/stats/gradient2.png">
**如何找到最快下山的路？**
- 假设此时山上的浓雾很大，下山的路无法确定;
- 假设你摔不死！
... | github_jupyter |
<a href="https://colab.research.google.com/github/Build-Week-Saltiest-Hack-News-Trolls-2/datascience/blob/Moly-malibu-patch-1/Sentimental_Analysis_pre_model.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#Sentimental Analysis Project:
```
!pip in... | github_jupyter |
# What is probability? A simulated introduction
```
#Import packages
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
sns.set()
```
## Learning Objectives of Part 1
- To have an understanding of what "probability" means, in both Bayesian and Frequentist ... | github_jupyter |
# hgvs Documention: Examples
This notebook is being drafted to run and review the code presented in the hgvs documentation that is in the "Creating a SequenceVariant from scratch" section (https://hgvs.readthedocs.io/en/stable/examples/creating-a-variant.html#overview).
## User Troubleshooting
Users proposed state.... | github_jupyter |
# *Quick, Draw!* GAN
In this notebook, we use Generative Adversarial Network code (adapted from [Rowel Atienza's](https://github.com/roatienza/Deep-Learning-Experiments/blob/master/Experiments/Tensorflow/GAN/dcgan_mnist.py) under [MIT License](https://github.com/roatienza/Deep-Learning-Experiments/blob/master/LICENSE)... | github_jupyter |
<a href="https://colab.research.google.com/github/AaronGe88inTHU/dreye-thu/blob/master/DataGenerator.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
from google.colab import drive
drive.mount('/content/drive')
import numpy as np
import cv2
from ... | github_jupyter |
<a href="https://colab.research.google.com/github/bruno-janota/DS-Unit-2-Linear-Models/blob/master/module1-regression-1/LS_DS_211.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Lambda School Data Science
*Unit 2, Sprint 1, Module 1*
---
# Regres... | github_jupyter |
# 執行語音轉文字服務操作
```
import azure.cognitiveservices.speech as speechsdk
# Creates an instance of a speech config with specified subscription key and service region.
# Replace with your own subscription key and region identifier from here: https://aka.ms/speech/sdkregion
speech_key, service_region = "196f2f318dc744049eaf... | github_jupyter |
```
import numpy as np
import gym
import k3d
from ratelimiter import RateLimiter
from k3d.platonic import Cube
from time import time
rate_limiter = RateLimiter(max_calls=4, period=1)
env = gym.make('CartPole-v0')
observation = env.reset()
plot = k3d.plot(grid_auto_fit=False, camera_auto_fit=False, grid=(-1,-1,-1,1,1... | github_jupyter |
**This notebook is an exercise in the [Python](https://www.kaggle.com/learn/python) course. You can reference the tutorial at [this link](https://www.kaggle.com/colinmorris/loops-and-list-comprehensions).**
---
# Try It Yourself
With all you've learned, you can start writing much more interesting programs. See if y... | github_jupyter |
```
import numpy as np
import pandas as pd
from sklearn.svm import SVC
from sklearn.decomposition import PCA
from sklearn.preprocessing import Imputer
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
DATA = 'datas... | github_jupyter |
<img align="left" src="https://lever-client-logos.s3.amazonaws.com/864372b1-534c-480e-acd5-9711f850815c-1524247202159.png" width=200>
<br></br>
<br></br>
## *Data Science Unit 1 Sprint 3 Assignment 1*
# Apply the t-test to real data
Your assignment is to determine which issues have "statistically significant" differ... | github_jupyter |
### Note
* Instructions have been included for each segment. You do not have to follow them exactly, but they are included to help you think through the steps.
```
# Dependencies and Setup
import pandas as pd
# File to Load
purchanse_file = "Resources/purchase_data.csv"
# Read Purchasing File and store into Pandas d... | github_jupyter |
```
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import pickle
import numpy as np
import cv2
from moviepy.editor import VideoFileClip
import math
import glob
class Left_Right:
last_L_points = []
last_R_points = []
def __init__(self, last_L_points, last_R_points):
self.last_L... | github_jupyter |
# 3 More Namespace Operations
### 3.1 `locals()` and `globals()`
Name binding operations covered so far:
- *name* `=` (assignment)
- `del` *name* (unbinds the name)
- `def` *name* function definition (including lambdas)
- `def name(`*names*`):` (function execution)
- *name*`.`*attribute_name* `=`, `__setat... | github_jupyter |
# Getting started
## Installing Python
It is recommended that you install the full Anaconda Python 3.8, as it set up your Python environment, together with a bunch of often used packages that you'll use during this course. A guide on installing Anaconda can be found here: https://docs.anaconda.com/anaconda/install/. ... | github_jupyter |
```
import numpy as np
import cv2
import matplotlib.pyplot as plt
import random
import os
img = cv2.imread("./map_bw.png")
Map = np.array(~(img[:,:,0]==0)).astype(int)
Navigable_terrain = np.array(Map.nonzero()).T
Sidx = random.sample(range(0,Navigable_terrain.shape[0]),1)
Eidx = Sidx
while (Sidx==Eidx):
Eidx = r... | github_jupyter |
### Basic Programming 13
### 1. Write a program that calculates and prints the value according to the given formula:
### Q = Square root of [(2 C D)/H]
### Following are the fixed values of C and H:
### C is 50. H is 30.
### D is the variable whose values should be input to your program in a comma-separated sequen... | github_jupyter |
```
!pip install efficientnet
#import the libraries needed
import pandas as pd
import numpy as np
import os
import cv2
from tqdm import tqdm_notebook as tqdm
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from keras_preprocessing.image import ImageDataGenerator
from tensorflo... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import math
import seaborn as sns
from sklearn import datasets
from sklearn import metrics
from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
... | github_jupyter |
```
# disable overly verbose tensorflow logging
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # or any {'0', '1', '2'}
import tensorflow as tf
import numpy as np
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.la... | github_jupyter |
```
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '3'
from tensor2tensor.data_generators import problem
from tensor2tensor.data_generators import text_problems
from tensor2tensor.data_generators import translate
from tensor2tensor.layers import common_attention
from tensor2tensor.utils import registry
from tensor2ten... | github_jupyter |
# Objective:
Classify Amazon food reviews using Random Forest Classifier.
We'll do the following exercises in this notebook
* Load the data stored in the format
1. BoW
2. Tfidf
3. Avg. W2V
4. Tfidf weighted W2V
* Divide the data in cross validation sets and find the optimal parameters... | github_jupyter |
# Preprocess "ROC Stories" for Story Completion
```
%load_ext autoreload
%autoreload 2
%matplotlib inline
import os
import glob
import pandas as pd
DATAPATH = '/path/to/ROCStories'
ROCstory_spring2016 = pd.read_csv(os.path.join(DATAPATH, "ROCStories__spring2016 - ROCStories_spring2016.csv"))
ROCstory_winter2017 = pd.... | github_jupyter |
```
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:85% !important; }</style>"))
import os
import time
import numpy as np
import pandas as pd
from os import listdir
from io import BytesIO
import requests
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras ... | github_jupyter |
# Introduction to the jupyter ecosystem & notebooks
## Before we get started ...
<br>
- most of what you’ll see within this lecture was prepared by Ross Markello, Michael Notter and Peer Herholz and further adapted for this course by Peer Herholz
- based on Tal Yarkoni's ["Introduction to Python" lecture at Neurohac... | github_jupyter |
## Use barcharts and heatmaps to visualize patterns in your data
IGN Game Reviews provide scores from experts for the most recent game releases, ranging from 0 (Disaster) to 10 (Masterpiece).
<img src="https://i.imgur.com/Oh06Fu1.png">
## Load the data
1. Read the IGN data file into a dataframe named `ign_scores`.
2... | github_jupyter |
<a href="https://colab.research.google.com/github/harmishpatel21/codesignal-IV-solutions/blob/main/code_signal_IV_solutions.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
**Code Signal Solution for Interview Challenges**
**Problem Statement 1:**
... | github_jupyter |
Fashion MNIST dataset
```
#!pip install --upgrade tensorflow
from __future__ import absolute_import, division, print_function, unicode_literals
# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras
# Helper libraries
import numpy as np
import matplotlib.pyplot as plt
print(tf.__version__)
`... | github_jupyter |
# Part 2: Introduction to Umami and the `Residual` Class
Umami is a package for calculating metrics for use with for Earth surface dynamics models. This notebook is the second notebook in a three-part introduction to using umami.
## Scope of this tutorial
Before starting this tutorial, you should have completed [Par... | github_jupyter |
```
import pickle as pk
import pandas as pd
%pylab inline
y_dic = pk.load(open("labelDic.cPickle","rb"))
X_dic = pk.load(open("vectorDicGDIpair.cPickle","rb"))
df = pd.read_csv('dida_v2_full.csv', index_col=0).replace('CO', 1).replace('TD', 0).replace('UK', -1)
rd = np.vectorize(lambda x: round(x * 10)/10)
essA_change... | github_jupyter |
# Operations on word vectors
Welcome to your first assignment of this week!
Because word embeddings are very computionally expensive to train, most ML practitioners will load a pre-trained set of embeddings.
**After this assignment you will be able to:**
- Load pre-trained word vectors, and measure similarity usi... | github_jupyter |
# Introduction #
In this exercise, you'll work through several applications of PCA to the [*Ames*](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data) dataset.
Run this cell to set everything up!
```
# Setup feedback system
from learntools.core import binder
binder.bind(globals())
from learnto... | github_jupyter |
<img width="10%" alt="Naas" src="https://landen.imgix.net/jtci2pxwjczr/assets/5ice39g4.png?w=160"/>
# LinkedIn - Send posts feed to gsheet
<a href="https://app.naas.ai/user-redirect/naas/downloader?url=https://raw.githubusercontent.com/jupyter-naas/awesome-notebooks/master/LinkedIn/LinkedIn_Send_posts_feed_to_gsheet.i... | github_jupyter |
---
# **Product Backorders**
---
## Introduction
A **product backorder** is a customer order that has not been fulfilled. Product backorder may be the result of strong sales performance (e.g. the product is in such high demand that production cannot keep up with sales). However, backorders can upset consumers, lead ... | github_jupyter |
# Regression Week 2: Multiple Regression (Interpretation)
The goal of this first notebook is to explore multiple regression and feature engineering with existing graphlab functions.
In this notebook you will use data on house sales in King County to predict prices using multiple regression. You will:
* Use SFrames to... | github_jupyter |
```
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
def N_single_qubit_gates_req_Rot(N_system_qubits, set_size):
return (2*N_system_qubits+1)*(set_size-1)
def N_CNOT_gates_req_Rot(N_system_qubits, set_size):
return 2*(N_system_qubits-1)*(set_size-1)
def N_cV_gates_req_LCU(N_system_qubits, s... | github_jupyter |
<a href="https://colab.research.google.com/github/prithwis/KKolab/blob/main/KK_B2_Hadoop_and_Hive.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
 trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits!
GANs were [first reported on](https://arxiv.org/abs/1406.2661) in 2014 from Ian Goodfellow and others in Yoshua Bengio'... | github_jupyter |
# FINN - Functional Verification of End-to-End Flow
-----------------------------------------------------------------
**Important: This notebook depends on the tfc_end2end_example notebook, because we are using models that are available at intermediate steps in the end-to-end flow. So please make sure the needed .onnx... | github_jupyter |
```
# dependencies and setup
from bs4 import BeautifulSoup as bs
from splinter import Browser
import time
import pandas as pd
# NEED TO CHANGE THE PATH TO MATCH YOUR COMPUTER
# showing the computer where to find the chromedriver
executable_path = {"executable_path": "/usr/local/bin/chromedriver"}
browser = Browser("chr... | github_jupyter |
```
import tensorflow as tf
from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession
config = ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.5
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)
# import the libraries as shown... | github_jupyter |
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