Famous Machine Learning Datasets - Machine Learning Wiki

• MNIST dataset, a collection of 70,000+ labeled digits, starting point of machine learning practice
• Beginner Machine Learning data
• Each image is 28 by 28 pixels so 784 data points per image
• Pixel value 0 to 255. Grayscale, zero means black, 255 means white or completely lit
• Often used in Google Tensorflow demos
• sklearn provides this dataset too
• Small images written by students teachers and government workers
• Inception-v3 pre-trained Inception-v3 model achieves state-of-the-art accuracy for recognizing general objects with 1000 classes, like "Zebra", "Dalmatian", and "Dishwasher"
• vgg19 image data
• What is VGG-16?
  
  
  "Since 2010, ImageNet has hosted an annual challenge where research teams present solutions to image classification and other tasks by training on the ImageNet dataset. ImageNet currently has millions of labeled images; it’s one of the largest high-quality image datasets in the world. The Visual Geometry group at the University of Oxford did really well in 2014 with two network architectures: VGG-16, a 16-layer convolutional Neural Network, and VGG-19, a 19-layer Convolutional Neural Network."
• Imagenet can output 1000+ classes. If we don't need that many, instead need transfer learning should consider replacing it with bottleneck of only 1-10 classes.
• Youtube 8M Video Data Kaggle https://www.kaggle.com/c/youtube8m
• 1000+ different objects in 1.3 million high resolution training images
• cornell movie dialog https://www.cs.cornell.edu/~cristian/Cornell_Movie-Dialogs_Corpus.html
• More famous datasets on github - amazing public databases https://github.com/caesar0301/awesome-public-datasets
• “Twenty Newsgroups” The 20 Newsgroups data set is a collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups. To the best of our knowledge, it was originally collected by Ken Lang, probably for his paper “Newsweeder: Learning to filter netnews,” though he does not explicitly mention this collection. The 20 newsgroups collection has become a popular data set for experiments in text applications of machine learning techniques, such as text classification and text clustering.

Additional datasets, some famous some lesser known

• Movie review https://grouplens.org/datasets/movielens/100k/ 100K ratings from 1000 users on 1700 movies
• Datasets on Keras

Inception - Tensorflow Wiki

• Google's state of art image classifier
• Pre-trained
• Open sourced
• Trained on 1.2 million images
• Training took 2 weeks

Time your python script

• Time it in the terminal

time file_name.py

• Time it in your script

import time
start = time.time()

fun()

print 'It took', time.time()-start, 'seconds.'

Source:StackOverflow - https://stackoverflow.com/questions/6786990/find-out-time-it-took-for-a-python-script-to-complete-execution

You can now open Jupyter Notebook in Coursera! - this week in online learning

Open Jupyter Notebook in Coursera

As seen in this Michigan Data Science MOOC, Coursera now allows you to open and edit Jupyter Notebook right in the browser. Pretty amazing engineer! Truly the future of learning. Think of it as a super Codecademy.com

Augmented Reality in Painting of Mona Lisa by Leonardo Da Vinci

Leonardo Da Vinci carefully studied the human anatomy of smiles and experimented with new painting techniques to create life like realistic smile of Mona Lisa. The smile is so illusive that it only is picked up by peripheral vision.

Python Interview List Slicing

a = [1,2,3,4] 
a[0:3] 
-> [1, 2, 3] 

 a[0:3:2] # slice with increment of 2 
->[1, 3] 

 a[::-1] # reverse slicing 
->[4, 3, 2, 1] 

 t=(1,2,3,4,5) #slicing tuples 
t[0:4] 
->(1, 2, 3, 4) 

 t=(1,2,3,4,5) 
sliceObj = slice(1,3) 
t[sliceObj] ->(2, 3)

t[:]
-> returns a full copy of the list

Preview of Flying Car Nanodegree Program from Udacity

Udacity's Flying Car Nanodegree trailer featuring Sebastian Thrun, KittyHawk flying drones, flying smart cars and more.