dansileshi

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

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dansileshi

#
#  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

dansileshi

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))

dansileshi

Visualizing Data using t-SNE

Introduction

• 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

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Curriculum Learning

Introduction

• 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.

Contributions of the paper