John Bencina jbencina

## CSPAN-transcript-parser.py
from html.parser import HTMLParser
import re
import json
import datetime as dt

class Entry():
    def __init__(self, time):
        self.time = ''
        self.time_base = time
        self.speaker = ''

## AmazonReview.ipynb

      
              1 file
            
          
              3 forks
            
          
              1 comment
            
          
              9 stars
            
          
                jbencina
                / AmazonReview.ipynb
            
            
              Created
              April 12, 2017 00:48
            
          
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## scikit_topic_cohesion.py
def topic_coherence(lda_model, corpus, num_words, sort_topics=True, avg_per_word=False):
    topics = {}

    # Get the top num_words within each model in topic
    for idx, topic in enumerate(lda_model.components_):
        topics[idx] = [i for i in topic.argsort()[:-num_words -1: -1]]

    # Convert to csc for efficient column slicing
    D = corpus.tocsc()


## tensorflow-simpsons.ipynb

      
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                jbencina
                / tensorflow-simpsons.ipynb
            
            
              Created
              June 1, 2017 03:43
            
              
                Using CNN TensorFlow to classify Simpsons images http://www.jbencina.com/blog/2017/05/31/getting-started-tensorflow/
              
          
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## backprop_tutorial.ipynb

      
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                jbencina
                / backprop_tutorial.ipynb
            
            
              Last active
              February 19, 2019 09:45
            
              
                Example for running backprop in numpy
              
          
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## plot.py
%matplotlib widget # Install ipympl https://github.com/matplotlib/ipympl
import matplotlib.pyplot as plt

# Previous steps followed from example notebook at
# https://github.com/facebookresearch/segment-anything/blob/main/notebooks/onnx_model_example.ipynb

def show_mask(mask, ax):
    # From SAM notebook
    color = np.array([30/255, 144/255, 255/255, 0.6])
    h, w = mask.shape[-2:]
	from html.parser import HTMLParser
	import re
	import json
	import datetime as dt

	class Entry():
	def __init__(self, time):
	self.time = ''
	self.time_base = time
	self.speaker = ''
	def topic_coherence(lda_model, corpus, num_words, sort_topics=True, avg_per_word=False):
	topics = {}

	# Get the top num_words within each model in topic
	for idx, topic in enumerate(lda_model.components_):
	topics[idx] = [i for i in topic.argsort()[:-num_words -1: -1]]

	# Convert to csc for efficient column slicing
	D = corpus.tocsc()
	%matplotlib widget # Install ipympl https://github.com/matplotlib/ipympl
	import matplotlib.pyplot as plt

	# Previous steps followed from example notebook at
	# https://github.com/facebookresearch/segment-anything/blob/main/notebooks/onnx_model_example.ipynb

	def show_mask(mask, ax):
	# From SAM notebook
	color = np.array([30/255, 144/255, 255/255, 0.6])
	h, w = mask.shape[-2:]