mil52603

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

mil52603

Large Scale Distributed Deep Networks

Introduction

• In machine learning, accuracy tends to increase with an increase in the number of training examples and number of model parameters.
• For large data, training becomes slow on even GPU (due to increase CPU-GPU data transfer).
• Solution: Distributed training and inference - DistBelief
• Link to paper

DistBelief

mil52603

from scene import *
from random import randint, random, choice
from sound import play_effect
from colorsys import hsv_to_rgb
from math import sin
from functools import partial
from copy import copy

class Star (object):
	def __init__(self):