Cedric Chee cedrickchee

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I am cedrickchee on github.
I am cedric (https://keybase.io/cedric) on keybase.
I have a public key ASDdukLhsM81xTVoQwh_1iXa9n6MFspqB5Wy4R6xGfjBxAo

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Computer Science and Mathematical Reasoning

The question is, what is intelligence and learning from the perspective of computer science and mathematical reasoning?

I think, prediction is the basic quality of intelligence, differentiation is the basic quality of learning.

Some important notes to self.

News Source

A copy of the Deep Tech Nexus PDF file we received in our email.

Mainstream News Coverage

Digital News Asia: SGInnovate unveils ‘Deep Tech Nexus’ strategy
ST Business Times: SGInnovate to invest in 20 start-ups, engage 15,000 participants under new deep tech strategy

Announcement of the unofficial release of part 1 v2: http://forums.fast.ai/t/unofficial-release-of-part-1-v2/9285
All the videos from part 1 v2 are now available. http://forums.fast.ai/t/welcome-to-part-1-v2/5787

How to get into this field of work where you can actually work on developing and applying Machine Learning algorithms every day?

The following advice is based on this reply on HN:

I'm probably the worst example of how to get into this field of work, but since I do actually work on developing and applying ML algorithms every day, I think my case might be relevant. Firstly, my background is not in mathematics or computer science what-so-ever; I'm a classically trained botanist who started came at the issue of programming, computer science, and ML from a perspective of "I've got questions I want to ask and techniques I want to apply that I'm currently under prepared to answer."

Working as a technician for the USDA, I learned programming (R and python) primarily because I needed a better way to deal with large data sets than excel (which prior to 5 years ago was all I used). At some point I put my foot down and decided I would go no further until I learned t


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	(tensorflow)cedric@starlite:~
	$ python -m tensorflow.models.image.mnist.convolutional
	I tensorflow/stream_executor/dso_loader.cc:101] successfully opened CUDA library libcublas.so.7.0 locally
	I tensorflow/stream_executor/dso_loader.cc:101] successfully opened CUDA library libcudnn.so.6.5 locally
	I tensorflow/stream_executor/dso_loader.cc:101] successfully opened CUDA library libcufft.so.7.0 locally
	I tensorflow/stream_executor/dso_loader.cc:101] successfully opened CUDA library libcuda.so locally
	I tensorflow/stream_executor/dso_loader.cc:101] successfully opened CUDA library libcurand.so.7.0 locally
	Extracting data/train-images-idx3-ubyte.gz
	Extracting data/train-labels-idx1-ubyte.gz
	Extracting data/t10k-images-idx3-ubyte.gz

	#########################################################################
	# This program is free software: you can redistribute it and/or modify #
	# it under the terms of the version 3 of the GNU General Public License #
	# as published by the Free Software Foundation. #
	# #
	# This program is distributed in the hope that it will be useful, but #
	# WITHOUT ANY WARRANTY; without even the implied warranty of #
	# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU #
	# General Public License for more details. #
	# #

	# Step - Submit Predictions
	# We have finished training and ready to run predictions on the test set.
	log_test_preds = learn.predict(is_test=True)

	# Convert log predictions to just probabilities (predictions).
	test_preds = np.exp(log_test_preds)

	# Create the submission file using the probabilities
	# Get a list of image file names from the test data loader
	im_fnames = data.test_dl.dataset.fnames

	// Use Gists to store code you would like to remember later on
	console.log(window); // log the "window" object to the console