05/22 - Deep Learning Hands-On Series - Data Exploration and API First Design 🎥
05/29 - Deep Learning Hands-On Series - Model Tuning 🎥
06/05 - Deep Learning Hands-On Series - Monitoring [cancelled]
05/22 - Deep Learning Hands-On Series - Data Exploration and API First Design 🎥
05/29 - Deep Learning Hands-On Series - Model Tuning 🎥
06/05 - Deep Learning Hands-On Series - Monitoring [cancelled]
Below is a list of commands and resources that work on Linux (some need installation of packages), would love to welcome contributions for the same for MacOSX and Windows platforms too. Please share and contribute back.
Please run the below in both vagrant
, docker
and other container environments and share your experiences with us!
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import numpy as np | |
def xgb_quantile_eval(preds, dmatrix, quantile=0.2): | |
""" | |
Customized evaluational metric that equals | |
to quantile regression loss (also known as | |
pinball loss). | |
Quantile regression is regression that |
from nltk.corpus import wordnet | |
from nltk.tokenize import word_tokenize | |
from random import randint | |
import nltk.data | |
# Load a text file if required | |
text = "Pete ate a large cake. Sam has a big mouth." | |
output = "" | |
# Load the pretrained neural net |