Pybelsberg is a project allowing constraint-based programming in Python using the Z3 theorem prover [1].
It is available on Github [2] and is licensed under the BSD 3-Clause License.
By Robert Lehmann, Christoph Matthies, Conrad Calmez, Thomas Hille.
See also Babelsberg/R [4] and Babelsberg/JS [5].
[1] https://github.com/Z3Prover/z3
[2] https://github.com/babelsberg/pybelsberg
[3] http://opensource.org/licenses/BSD-3-Clause
[4] https://github.com/timfel/babelsberg-r
[5] https://github.com/timfel/babelsberg-js
JSON is preferred by web and mobile developers because it is simple and flexible. It is human readable and semi-structured. JSON is easy to use. DBAs prefer relational databases because they are reliable and secure – enforcing data integrity via constraints and transactions. What's the best way to support developers and DBAs without deploying two separate databases, further complicating things?In this webinar, we'll explore the use of hybrid data models (relational + JSON) with MariaDB TX to provide faster schema evolution / more schema flexibility, and walk through 22 examples showing how hybrid data models can be created, indexed and queried. MariaDB TX includes a comprehensive set of JSON functions for reading, writing and querying JSON documents – and converting rows to JSON documents and vice-versa.
You will learn:
The pros and cons of using semi-structured data in a relational database
The capabilities and limitations of JSON functions for SQL
How to validate JSON documents and maintain data integrity
Pybelsberg is a project allowing constraint-based programming in Python using the Z3 theorem prover [1].
It is available on Github [2] and is licensed under the BSD 3-Clause License.
By Robert Lehmann, Christoph Matthies, Conrad Calmez, Thomas Hille.
See also Babelsberg/R [4] and Babelsberg/JS [5].
[1] https://github.com/Z3Prover/z3
[2] https://github.com/babelsberg/pybelsberg
[3] http://opensource.org/licenses/BSD-3-Clause
[4] https://github.com/timfel/babelsberg-r
[5] https://github.com/timfel/babelsberg-js
JSON is preferred by web and mobile developers because it is simple and flexible. It is human readable and semi-structured. JSON is easy to use. DBAs prefer relational databases because they are reliable and secure – enforcing data integrity via constraints and transactions. What's the best way to support developers and DBAs without deploying two separate databases, further complicating things?In this webinar, we'll explore the use of hybrid data models (relational + JSON) with MariaDB TX to provide faster schema evolution / more schema flexibility, and walk through 22 examples showing how hybrid data models can be created, indexed and queried. MariaDB TX includes a comprehensive set of JSON functions for reading, writing and querying JSON documents – and converting rows to JSON documents and vice-versa.
You will learn:
The pros and cons of using semi-structured data in a relational database
The capabilities and limitations of JSON functions for SQL
How to validate JSON documents and maintain data integrity
Lucio Floretta - TensorFlow and Deep Learning without a PhD - Codemotion Mila...Codemotion
With TensorFlow, deep machine learning transitions from an area of research to mainstream software engineering. In this session, we'll work together to construct and train a neural network that recognises handwritten digits. Along the way, we'll discover some of the "tricks of the trade" used in neural network design, and finally, we'll bring the recognition accuracy of our model above 99%.
Video: https://youtu.be/dYhrCUFN0eM
Article: https://medium.com/p/the-gentlest-introduction-to-tensorflow-248dc871a224
Code: https://github.com/nethsix/gentle_tensorflow/blob/master/code/linear_regression_one_feature.py
This alternative introduction to Google's official Tensorflow (TF) tutorial strips away the unnecessary concepts that overly complicates getting started. The goal is to use TF to perform Linear Regression (LR) that has only a single-feature. We show how to model the LR using a TF graph, how to define the cost function to measure how well the an LR model fits the dataset, and finally train the LR model to find the best fit model.
Explanation on Tensorflow example -Deep mnist for expert홍배 김
you can find the exact and detailed network architecture of 'Deep mnist for expert' example of tensorflow's tutorial. I also added descriptions on the program for your better understanding.
Gentlest Introduction to Tensorflow - Part 3Khor SoonHin
Articles:
* https://medium.com/all-of-us-are-belong-to-machines/gentlest-intro-to-tensorflow-part-3-matrices-multi-feature-linear-regression-30a81ebaaa6c
* https://medium.com/all-of-us-are-belong-to-machines/gentlest-intro-to-tensorflow-4-logistic-regression-2afd0cabc54
Video: https://youtu.be/F8g_6TXKlxw
Code: https://github.com/nethsix/gentle_tensorflow
In this part, we:
* Use Tensorflow for linear regression models with multiple features
* Use Tensorflow for logistic regression models with multiple features. Specifically:
* Predict multi-class/discrete outcome
* Explain why we use cross-entropy as cost function
* Explain why we use softmax
* Tensorflow Cheatsheet #1
* Single feature linear regression
* Multi-feature linear regression
* Multi-feature logistic regression
This book is intended for education and fun. Python is an amazing, text-based coding language, perfectly suited for children older than the age of 10. The Standard Python library has a module called Turtle which is a popular way to introduce programming to kids. This library enables children to create pictures and shapes by providing them with a virtual canvas. With the Python Turtle library, you can create nice animation projects using images that are taken from the internet, scaled-down stored as a gif-files download to the projects. The book includes 19 basic lessons with examples that introduce to the Python codes through Turtle library which is convenient to the school students of 10+years old. The book has also a lot of projects that show how to make different animations with Turtle graphics: games, applications to math, physics, and science.
A tour of Python: slides from presentation given in 2012.
[Some slides are not properly rendered in SlideShare: the original is still available at http://www.aleksa.org/2015/04/python-presentation_7.html.]
TensorFlow is a wonderful tool for rapidly implementing neural networks. In this presentation, we will learn the basics of TensorFlow and show how neural networks can be built with just a few lines of code. We will highlight some of the confusing bits of TensorFlow as a way of developing the intuition necessary to avoid common pitfalls when developing your own models. Additionally, we will discuss how to roll our own Recurrent Neural Networks. While many tutorials focus on using built in modules, this presentation will focus on writing neural networks from scratch enabling us to build flexible models when Tensorflow’s high level components can’t quite fit our needs.
About Nathan Lintz:
Nathan Lintz is a research scientist at indico Data Solutions where he is responsible for developing machine learning systems in the domains of language detection, text summarization, and emotion recognition. Outside of work, Nathan is currently writting a book on TensorFlow as an extension to his tutorial repository https://github.com/nlintz/TensorFlow-Tutorials
Link to video https://www.youtube.com/watch?v=op1QJbC2g0E&feature=youtu.be
Keynote presented at European Testing Conference (9th February 2017)
What happens when things break? What happens when software fails? We regard it as a normal and personal inconvenience when apps crash or servers become unavailable, but what are the implications beyond the individual user? Is software reliability simply a business decision or does it have economic, social and cultural consequences? What are the moral and practical implications for software developers? And when we talk of ‘systems’, are we part of the ‘system’? What about the bugs on our side of the keyboard? In this talk we will explore examples of failures in software and its application, and how they affect us at different scales, from user to society.
Gentlest Introduction to Tensorflow - Part 2Khor SoonHin
Video: https://youtu.be/Trc52FvMLEg
Article: https://medium.com/@khor/gentlest-introduction-to-tensorflow-part-2-ed2a0a7a624f
Code: https://github.com/nethsix/gentle_tensorflow
Continuing from Part 1 where we used Tensorflow to perform linear regression for a model with single feature, here we:
* Use Tensorboard to visualize linear regression variables and the Tensorflow network graph
* Perform stochastic/mini-batch/batch gradient descent
JSON is the de facto standard for consuming and producing data from web, mobile and IoT apps, but relational databases are required for reliability – enforcing data integrity and providing durability with transactions. They're not mutually exclusive.
MariaDB TX introduced SQL functions for validating, indexing and querying JSON documents – and for returning relational data as JSON documents, or JSON documents as relational data.
In this webinar, we'll show you how to create and query flexible schemas based on hybrid data models (relational + JSON) so you can get the best of both worlds.
You will learn:
The pros and cons of using semi-structured data
The capabilities and limitations of JSON
How to validate JSON and maintain data integrity
Where and when to use JSON documents
Lucio Floretta - TensorFlow and Deep Learning without a PhD - Codemotion Mila...Codemotion
With TensorFlow, deep machine learning transitions from an area of research to mainstream software engineering. In this session, we'll work together to construct and train a neural network that recognises handwritten digits. Along the way, we'll discover some of the "tricks of the trade" used in neural network design, and finally, we'll bring the recognition accuracy of our model above 99%.
Video: https://youtu.be/dYhrCUFN0eM
Article: https://medium.com/p/the-gentlest-introduction-to-tensorflow-248dc871a224
Code: https://github.com/nethsix/gentle_tensorflow/blob/master/code/linear_regression_one_feature.py
This alternative introduction to Google's official Tensorflow (TF) tutorial strips away the unnecessary concepts that overly complicates getting started. The goal is to use TF to perform Linear Regression (LR) that has only a single-feature. We show how to model the LR using a TF graph, how to define the cost function to measure how well the an LR model fits the dataset, and finally train the LR model to find the best fit model.
Explanation on Tensorflow example -Deep mnist for expert홍배 김
you can find the exact and detailed network architecture of 'Deep mnist for expert' example of tensorflow's tutorial. I also added descriptions on the program for your better understanding.
Gentlest Introduction to Tensorflow - Part 3Khor SoonHin
Articles:
* https://medium.com/all-of-us-are-belong-to-machines/gentlest-intro-to-tensorflow-part-3-matrices-multi-feature-linear-regression-30a81ebaaa6c
* https://medium.com/all-of-us-are-belong-to-machines/gentlest-intro-to-tensorflow-4-logistic-regression-2afd0cabc54
Video: https://youtu.be/F8g_6TXKlxw
Code: https://github.com/nethsix/gentle_tensorflow
In this part, we:
* Use Tensorflow for linear regression models with multiple features
* Use Tensorflow for logistic regression models with multiple features. Specifically:
* Predict multi-class/discrete outcome
* Explain why we use cross-entropy as cost function
* Explain why we use softmax
* Tensorflow Cheatsheet #1
* Single feature linear regression
* Multi-feature linear regression
* Multi-feature logistic regression
This book is intended for education and fun. Python is an amazing, text-based coding language, perfectly suited for children older than the age of 10. The Standard Python library has a module called Turtle which is a popular way to introduce programming to kids. This library enables children to create pictures and shapes by providing them with a virtual canvas. With the Python Turtle library, you can create nice animation projects using images that are taken from the internet, scaled-down stored as a gif-files download to the projects. The book includes 19 basic lessons with examples that introduce to the Python codes through Turtle library which is convenient to the school students of 10+years old. The book has also a lot of projects that show how to make different animations with Turtle graphics: games, applications to math, physics, and science.
A tour of Python: slides from presentation given in 2012.
[Some slides are not properly rendered in SlideShare: the original is still available at http://www.aleksa.org/2015/04/python-presentation_7.html.]
TensorFlow is a wonderful tool for rapidly implementing neural networks. In this presentation, we will learn the basics of TensorFlow and show how neural networks can be built with just a few lines of code. We will highlight some of the confusing bits of TensorFlow as a way of developing the intuition necessary to avoid common pitfalls when developing your own models. Additionally, we will discuss how to roll our own Recurrent Neural Networks. While many tutorials focus on using built in modules, this presentation will focus on writing neural networks from scratch enabling us to build flexible models when Tensorflow’s high level components can’t quite fit our needs.
About Nathan Lintz:
Nathan Lintz is a research scientist at indico Data Solutions where he is responsible for developing machine learning systems in the domains of language detection, text summarization, and emotion recognition. Outside of work, Nathan is currently writting a book on TensorFlow as an extension to his tutorial repository https://github.com/nlintz/TensorFlow-Tutorials
Link to video https://www.youtube.com/watch?v=op1QJbC2g0E&feature=youtu.be
Keynote presented at European Testing Conference (9th February 2017)
What happens when things break? What happens when software fails? We regard it as a normal and personal inconvenience when apps crash or servers become unavailable, but what are the implications beyond the individual user? Is software reliability simply a business decision or does it have economic, social and cultural consequences? What are the moral and practical implications for software developers? And when we talk of ‘systems’, are we part of the ‘system’? What about the bugs on our side of the keyboard? In this talk we will explore examples of failures in software and its application, and how they affect us at different scales, from user to society.
Gentlest Introduction to Tensorflow - Part 2Khor SoonHin
Video: https://youtu.be/Trc52FvMLEg
Article: https://medium.com/@khor/gentlest-introduction-to-tensorflow-part-2-ed2a0a7a624f
Code: https://github.com/nethsix/gentle_tensorflow
Continuing from Part 1 where we used Tensorflow to perform linear regression for a model with single feature, here we:
* Use Tensorboard to visualize linear regression variables and the Tensorflow network graph
* Perform stochastic/mini-batch/batch gradient descent
JSON is the de facto standard for consuming and producing data from web, mobile and IoT apps, but relational databases are required for reliability – enforcing data integrity and providing durability with transactions. They're not mutually exclusive.
MariaDB TX introduced SQL functions for validating, indexing and querying JSON documents – and for returning relational data as JSON documents, or JSON documents as relational data.
In this webinar, we'll show you how to create and query flexible schemas based on hybrid data models (relational + JSON) so you can get the best of both worlds.
You will learn:
The pros and cons of using semi-structured data
The capabilities and limitations of JSON
How to validate JSON and maintain data integrity
Where and when to use JSON documents
It covers- Introduction to R language, Creating, Exploring data with Various Data Structures e.g. Vector, Array, Matrices, and Factors. Using Methods with examples.
INFORMATIVE ESSAYThe purpose of the Informative Essay assignme.docxcarliotwaycave
INFORMATIVE ESSAY
The purpose of the Informative Essay assignment is to choose a job or task that you know how to do and then write a minimum of 2 full pages, maximum of 3 full pages, Informative Essay teaching the reader how to do that job or task. You will follow the organization techniques explained in Unit 6.
Here are the details:
1. Read the Lecture Notes in Unit 6. You may also find the information in Chapter 10.5 in our text on Process Analysis helpful. The lecture notes will really be the most important to read in writing this assignment. However, here is a link to that chapter that you may look at in addition to the lecture notes:
https://open.lib.umn.edu/writingforsuccess/chapter/10-5-process-analysis/ (Links to an external site.)
2. Choose your topic, that is, the job or task you want to teach. As the notes explain, this should be a job or task that you already know how to do, and it should be something you can do well. At this point, think about your audience (reader). Will your reader need any knowledge or experience to do this job or task, or will you write these instructions for a general reader where no experience is required to perform the job?
3. Plan your outline to organize this essay. Unit 6 notes offer advice on this organization process. Be sure to include an introductory paragraph that has the four main points presented in the lecture notes.
4. Write the essay. It will need to be at least 2 FULL pages long, maximum of 3 full pages long. You will use the MLA formatting that you used in previous essays from Units 3, 4, and 5.
5. Be sure to include a title for your essay.
6. After writing the essay, be sure to take time to read it several times for revision and editing. It would be helpful to have at least one other person proofread it as well before submitting the assignment.
Quiz2
# comments start with #
# to quit q()
# two steps to install any library
#install.packages("rattle")
#library(rattle)
setwd("D:/AJITH/CUMBERLANDS/Ph.D/SEMESTER 3/Data Science & Big Data Analy (ITS-836-51)/RStudio/Week2")
getwd()
x <- 3 # x is a vector of length 1
print(x)
v1 <- c(2,4,6,8,10)
print(v1)
print(v1[3])
v <- c(1:10) #creates a vector of 10 elements numbered 1 through 10. More complicated data
print(v)
print(v[6])
# Import test data
test<-read.csv("CVEs.csv")
test1<-read.csv("CVEs.csv", sep=",")
test2<-read.table("CVEs.csv", sep=",")
write.csv(test2, file="out.csv")
# Write CSV in R
write.table(test1, file = "out1.csv",row.names=TRUE, na="",col.names=TRUE, sep=",")
head(test)
tail(test)
summary(test)
head <- head(test)
tail <- tail(test)
cor(test$X, test$index)
sd(test$index)
var(test$index)
plot(test$index)
hist(test$index)
str(test$index)
quit()
Quiz3
setwd("C:/Users/ialsmadi/Desktop/University_of_Cumberlands/Lectures/Week2/RScripts")
getwd()
# Import test data
data<-read.csv("yearly_sales.csv")
#A 5-number summary is a set of 5 descriptive statistics for summarizing a continuous univariate data set.
#It consists o ...
Christian Gill ''Functional programming for the people''OdessaJS Conf
There is a lot of mystery around functional programming. Even though libraries like React and Redux brought some of its concepts to JavaScript developers, there is still a lot more to be discovered and many benefits that we can gain from it.
We'll go through some of the principles and fundamentals of functional programing and how we can leverage them to write composable and declarative JavaScript code.
Final tagless. The topic strikes fear into the hearts of Scala developers everywhere—and not without reason. Final tagless allows developers to build composable Domain Specific Languages (DSLs) that model interaction with the outside world. Programs written using the final tagless style can be tested deterministically and reasoned about at compile-time. Yet the technique requires confusing, compiler-choking higher-kinded types, like `F[_]`, and pervasive, non-inferable context bounds like `F[_]: Concurrent: Console: Logging`. Many have looked at final tagless and wondered if all the layers of complexity and ceremony are really worth the benefits.
In this presentation, John A. De Goes provides a gentle and accessible introduction to final tagless, explaining what it is and the problem it intends to solve. John shows that while final tagless is easier to use than free monads, the technique suffers from a litany of drawbacks that push developers away from functional programming in Scala. John then introduces a novel approach that shares some of the benefits of final tagless, but which is idiomatic Scala, easy to explain, doesn’t need any complex type machinery, provides flawless type inference, and works beautifully across Scala 2.x and Scala 3.
Come join John for an evening of fun as you learn how to write functional code in Scala that's easy to test and easy to reason about—all without the complexity of free monads or final tagless.
An overview of two types of graph databases: property databases and knowledge/RDF databases, together with their dominant respective query languages, Cypher and SPARQL. Also a quick look at some property DB frameworks, including TinkerPop and its query language, Gremlin.
6. calculate_prize <- function(windows) {
payoffs <- c("DD" = 800, "7" = 80, "BBB" = 40,
arguments
name = 25, "B" = 10, "C" = 10, "0" = 0)
"BB"
same <- length(unique(windows)) == 1
allbars <- all(windows %in% c("B", "BB", "BBB"))
if (same) {
prize <- payoffs[windows[1]]
} else if (allbars) {
prize <- 5
} else {
always indent <- sum(windows == "C")
cherries
inside {}! <- sum(windows == "DD")
diamonds
prize <- c(0, 2, 5)[cherries + 1] *
c(1, 2, 4)[diamonds + 1]
}
prize
}
last value in function is result
Monday, 13 September 2010
7. mean <- function(x) {
sum(x) / length(x)
}
mean(1:10)
mean <- function(x, na.rm = TRUE) {
if (na.rm) x <- x[!is.na(x)]
sum(x) / length(x)
}
mean(c(NA, 1:9))
Monday, 13 September 2010
8. mean <- function(x) {
sum(x) / length(x)
}
mean(1:10)
default value
mean <- function(x, na.rm = TRUE) {
if (na.rm) x <- x[!is.na(x)]
sum(x) / length(x)
}
mean(c(NA, 1:9))
Monday, 13 September 2010
9. # Function: mean
str(mean)
mean
args(mean)
formals(mean)
body(mean)
Monday, 13 September 2010
10. Function arguments
Every function argument has a name and
a position. When calling, matches exact
name, then partial name, then position.
Rule of thumb: for common functions,
position based matching is ok for required
arguments. Otherwise use names
(abbreviated as long as clear).
Monday, 13 September 2010
11. mean(c(NA, 1:9), na.rm = T)
# Confusing in this case, but often saves typing
mean(c(NA, 1:9), na = T)
# Generally don't need to name all arguments
mean(x = c(NA, 1:9), na.rm = T)
# Unusual orders best avoided, but
# illustrate the principle
mean(na.rm = T, c(NA, 1:9))
mean(na = T, c(NA, 1:9))
Monday, 13 September 2010
12. # Overkill
qplot(x = price, y = carat, data = diamonds)
# Don't need to supply defaults
mean(c(NA, 1:9), na.rm = F)
# Need to remember too much about mean
mean(c(NA, 1:9), , T)
# Don't abbreviate too much!
mean(c(NA, 1:9), n = T)
Monday, 13 September 2010
14. Your turn
Write a function to calculate the variance
of a vector. Make sure it has a na.rm
argument.
Write the function in your text editor, then
copy into R.
Monday, 13 September 2010
15. Strategy
Always want to start simple: start with
test values and get the body of the
function working first.
Check each step as you go.
Don’t try and do too much at once!
Create the function once everything
works.
Monday, 13 September 2010
16. x <- 1:10
sum((x - mean(x)) ^ 2) / (length(x) - 1)
var <- function(x) sum((x - mean(x)) ^ 2) / (length(x) - 1)
x <- c(1:10, NA)
var(x)
na.rm <- TRUE
if (na.rm) {
x <- x[!is.na(x)]
}
var <- function(x, na.rm = F) {
if (na.rm) {
x <- x[!is.na(x)]
}
sum((x - mean(x)) ^ 2) / (length(x) - 1)
}
Monday, 13 September 2010
17. Testing
Always a good idea to test your code.
We have a prebuilt set of test cases: the
prize column in slots.csv
So for each row in slots.csv, we need to
calculate the prize and compare it to the
actual. (Hopefully they will be same!)
Monday, 13 September 2010
18. For loops
print(1)
print(2)
print(3) for(value in 1:10)
print(4) {
print(5) print(value)
print(6) }
print(7)
print(8)
print(9)
print(10)
Monday, 13 September 2010
19. For loops
cuts <- levels(diamonds$cut)
for(cut in cuts) {
selected <- diamonds$price[diamonds$cut == cut]
print(cut)
print(mean(selected))
}
# Have to do something with output!
Monday, 13 September 2010
20. # Common pattern: create object for output,
# then fill with results
cuts <- levels(diamonds$cut)
means <- rep(NA, length(cuts))
for(i in seq_along(cuts)) {
sub <- diamonds[diamonds$cut == cuts[i], ]
means[i] <- mean(sub$price)
}
# We will learn more sophisticated ways to do this
# later on, but this is the most explicit
Monday, 13 September 2010
21. # Common pattern: create object for output,
# then fill with results
cuts <- levels(diamonds$cut)
means <- rep(NA, length(cuts))
for(i in seq_along(cuts)) { Why use i and not cut?
sub <- diamonds[diamonds$cut == cuts[i], ]
means[i] <- mean(sub$price)
}
# We will learn more sophisticated ways to do this
# later on, but this is the most explicit
Monday, 13 September 2010
22. seq_len(5)
seq_len(10)
n <- 10
seq_len(10)
seq_along(1:10)
seq_along(1:10 * 2)
Monday, 13 September 2010
23. Your turn
For each diamond colour, calculate the
median price and carat size
Monday, 13 September 2010
25. Back to slots...
For each row, calculate the prize and save
it, then compare calculated prize to actual
prize
Question: given a row, how can we
extract the slots in the right form for the
function?
Monday, 13 September 2010
27. # Create space to put the results
slots$check <- NA
# For each row, calculate the prize
for(i in seq_len(nrow(slots))) {
w <- as.character(slots[i, 1:3])
slots$check[i] <- calculate_prize(w)
}
# Check with known answers
subset(slots, prize != prize_c)
# Uh oh!
Monday, 13 September 2010
28. # Create space to put the results
slots$check <- NA
# For each row, calculate the prize
for(i in seq_len(nrow(slots))) {
w <- as.character(slots[i, 1:3])
slots$check[i] <- calculate_prize(w)
}
# Check with known answers
subset(slots, prize != prize_c)
# Uh oh!
What is the problem? Think
about the most general case
Monday, 13 September 2010
29. DD DD DD 800
windows <- c("DD", "C", "C")
7 7 7 80 # How can we calculate the
BBB BBB BBB 40 # payoff?
BB BB BB 25
B B B 10
C C C 10
Any bar Any bar Any bar 5
C C * 5
C * C 5
C * * 2
DD doubles any winning
* C * 2 combination. Two DD
* * C 2 quadruples. DD is wild
Monday, 13 September 2010
30. Homework
What did we miss? Find out the problem
with our scoring function and fix it.
Monday, 13 September 2010