Sriharsh Bhyravajjula darthbhyrava

February 2018

Using the lambda in python. lambda x: clean_text(x)
re.MULTILINE
hashtag segmentation/splitting?
sklearn.feature_extraction, sklearn.decomposition,
Pandas df series?
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Biggies

Learn Pandas

Building TensorFlow from source (TF 2.3.0, Ubuntu 20.04)

Why build from source?

The official instructions on installing TensorFlow are here: https://www.tensorflow.org/install. If you want to install TensorFlow just using pip, you are running a supported Ubuntu LTS distribution, and you're happy to install the respective tested CUDA versions (which often are outdated), by all means go ahead. A good alternative may be to run a Docker image.

I am usually unhappy with installing what in effect are pre-built binaries. These binaries are often not compatible with the Ubuntu version I am running, the CUDA version that I have installed, and so on. Furthermore, they may be slower than binaries optimized for the target architecture, since certain instructions are not being used (e.g. AVX2, FMA).

So installing TensorFlow from source becomes a necessity. The official instructions on building TensorFlow from source are here: ht

	# Helper function to plot a decision boundary.
	# If you don't fully understand this function don't worry, it just generates the contour plot below.
	def plot_decision_boundary(pred_func):
	# Set min and max values and give it some padding
	x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
	y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
	h = 0.01
	# Generate a grid of points with distance h between them
	xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
	# Predict the function value for the whole gid

	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)