Daniel Asfaw dansileshi

Visualizing Data using t-SNE

Method to visualize high-dimensional data points in 2/3 dimensional space.
Data visualization techniques like Chernoff faces and graph approaches just provide a representation and not an interpretation.
Dimensionality reduction techniques fail to retain both local and global structure of the data simultaneously. For example, PCA and MDS are linear techniques and fail on data lying on a non-linear manifold.
t-SNE approach converts data into a matrix of pairwise similarities and visualizes this matrix.
Based on SNE (Stochastic Neighbor Embedding)
Link to paper

Curriculum Learning - When training machine learning models, start with easier subtasks and gradually increase the difficulty level of the tasks.
Motivation comes from the observation that humans and animals seem to learn better when trained with a curriculum like a strategy.
Link to the paper.

	from keras.models import Model
	from keras.layers import (
	Input,
	Dense,
	Flatten,
	merge,
	Lambda
	)
	from keras.layers.convolutional import (
	Convolution2D,

	#
	# mnist_cnn_bn.py date. 5/21/2016
	#

	from __future__ import absolute_import
	from __future__ import division
	from __future__ import print_function

	import os
	import numpy as np


	import tensorflow as tf
	import numpy as np

	FC_SIZE = 1024
	DTYPE = tf.float32


	def _weight_variable(name, shape):
	return tf.get_variable(name, shape, DTYPE, tf.truncated_normal_initializer(stddev=0.1))