prompt stringlengths 501 4.98M | target stringclasses 1
value | chunk_prompt bool 1
class | kind stringclasses 2
values | prob float64 0.2 0.97 ⌀ | path stringlengths 10 394 ⌀ | quality_prob float64 0.4 0.99 ⌀ | learning_prob float64 0.15 1 ⌀ | filename stringlengths 4 221 ⌀ |
|---|---|---|---|---|---|---|---|---|
```
#export
from fastai.basics import *
from fastai.tabular.core import *
from fastai.tabular.model import *
from fastai.tabular.data import *
#hide
from nbdev.showdoc import *
#default_exp tabular.learner
```
# Tabular learner
> The function to immediately get a `Learner` ready to train for tabular data
The main fu... | true | code | 0.704262 | null | null | null | null | |
# Aerospike Connect for Spark - SparkML Prediction Model Tutorial
## Tested with Java 8, Spark 3.0.0, Python 3.7, and Aerospike Spark Connector 3.0.0
## Summary
Build a linear regression model to predict birth weight using Aerospike Database and Spark.
Here are the features used:
- gestation weeks
- mother’s age
- fat... | true | code | 0.475301 | null | null | null | null | |
# Classification on Iris dataset with sklearn and DJL
In this notebook, you will try to use a pre-trained sklearn model to run on DJL for a general classification task. The model was trained with [Iris flower dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set).
## Background
### Iris Dataset
The dataset c... | true | code | 0.782642 | null | null | null | null | |
<a href="https://colab.research.google.com/github/satyajitghana/TSAI-DeepNLP-END2.0/blob/main/09_NLP_Evaluation/ClassificationEvaluation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
! pip3 install git+https://github.com/extensive-nlp/ttc_nlp ... | true | code | 0.862265 | null | null | null | null | |
## Accessing TerraClimate data with the Planetary Computer STAC API
[TerraClimate](http://www.climatologylab.org/terraclimate.html) is a dataset of monthly climate and climatic water balance for global terrestrial surfaces from 1958-2019. These data provide important inputs for ecological and hydrological studies at g... | true | code | 0.609059 | null | null | null | null | |
```
import numpy as np
import matplotlib.pyplot as plt
import numba
from tqdm import tqdm
import eitest
```
# Data generators
```
@numba.njit
def event_series_bernoulli(series_length, event_count):
'''Generate an iid Bernoulli distributed event series.
series_length: length of the event series
event_cou... | true | code | 0.687079 | null | null | null | null | |
# Chapter 4
`Original content created by Cam Davidson-Pilon`
`Ported to Python 3 and PyMC3 by Max Margenot (@clean_utensils) and Thomas Wiecki (@twiecki) at Quantopian (@quantopian)`
______
## The greatest theorem never told
This chapter focuses on an idea that is always bouncing around our minds, but is rarely ma... | true | code | 0.669259 | null | null | null | null | |
<a href="https://colab.research.google.com/github/s-mostafa-a/pytorch_learning/blob/master/simple_generative_adversarial_net/MNIST_GANs.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import torch
from torchvision.transforms import ToTensor, Nor... | true | code | 0.824197 | null | null | null | null | |
# Tutorial 2. Solving a 1D diffusion equation
```
# Document Author: Dr. Vishal Sharma
# Author email: sharma_vishal14@hotmail.com
# License: MIT
# This tutorial is applicable for NAnPack version 1.0.0-alpha4
```
### I. Background
The objective of this tutorial is to present the step-by-step solution of a 1D diffus... | true | code | 0.849379 | null | null | null | null | |
# Monte Carlo Integration with Python
## Dr. Tirthajyoti Sarkar ([LinkedIn](https://www.linkedin.com/in/tirthajyoti-sarkar-2127aa7/), [Github](https://github.com/tirthajyoti)), Fremont, CA, July 2020
---
### Disclaimer
The inspiration for this demo/notebook stemmed from [Georgia Tech's Online Masters in Analytics (... | true | code | 0.547101 | null | null | null | null | |
This illustrates the datasets.make_multilabel_classification dataset generator. Each sample consists of counts of two features (up to 50 in total), which are differently distributed in each of two classes.
Points are labeled as follows, where Y means the class is present:
| 1 | 2 | 3 | Color |
|--- |--- |--- |--... | true | code | 0.612194 | null | null | null | null | |
[Table of Contents](http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/table_of_contents.ipynb)
# Kalman Filter Math
```
#format the book
%matplotlib inline
from __future__ import division, print_function
from book_format import load_style
load_style()
```
If you've gotten th... | true | code | 0.608507 | null | null | null | null | |
# Estimation on real data using MSM
```
from consav import runtools
runtools.write_numba_config(disable=0,threads=4)
%matplotlib inline
%load_ext autoreload
%autoreload 2
# Local modules
from Model import RetirementClass
import figs
import SimulatedMinimumDistance as SMD
# Global modules
import numpy as np
import p... | true | code | 0.489076 | null | null | null | null | |
<a href="https://colab.research.google.com/github/clemencia/ML4PPGF_UERJ/blob/master/Exemplos_DR/Exercicios_DimensionalReduction.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#Mais Exercícios de Redução de Dimensionalidade
Baseado no livro "Pytho... | true | code | 0.712876 | null | null | null | null |
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