bigsnarfdude

"""
VISUALISE THE LIDAR DATA FROM THE KITTI DATASET

Based on the sample code from
    https://github.com/utiasSTARS/pykitti/blob/master/demos/demo_raw.py
And:
    http://stackoverflow.com/a/37863912

Contains two methods of visualizing lidar data interactively.
 - Matplotlib - very slow, and likely to crash, so only 1 out of every 100

bigsnarfdude

name: "GTANet"

# Can be used with pretrained Caffenet (AlexNet architecture).
# Layers with names containing 'gtanet' are not transferred from Caffenet.

layer {
  name: "gta_frames_input_layer"
  type: "HDF5Data"
  top: "images"
  top: "targets"

bigsnarfdude

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.

It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data

In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/

bigsnarfdude

"""
ubuntu@red:~/dev/catDogs$ THEANO_FLAGS=device=gpu,floatX=float32 python script_2.py
"""


import os
import h5py
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential

bigsnarfdude

"""
ubuntu@red:~/dev/catDogs$ THEANO_FLAGS=device=gpu,floatX=float32 python script_1.py
"""

from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Convolution2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense

bigsnarfdude

1. General Background and Overview

• Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
• Models and Issues in Data Stream Systems
• Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep