Here, we have a simple neural network described in my slides about neural networks... It is using simple concepts from linear algebra to encapsulate the complexities (This makes possible to even use parallel matrix multiplication and some other algorithms to make everything faster) and making everything more modular and compact.
The data sets are coming from http://yann.lecun.com/exdb/mnist/.
beyond tellerrand: Mobile Apps with JavaScript – There's More Than WebHeiko Behrens
abstract from http://2011.beyondtellerrand.com
Modern web technologies and responsive design aim at a platform independent code base while promising first-class experience on any mobile device. Even though purely web-based approaches can achieve stunning results, they (still) cannot compete with their native counterpart regarding platform features and integration.
In this talk, I will show you how we can use JavaScript to produce mobile apps that include features such as native UI, push notifications, sensors, and paid distribution. You can expect lots of live demos when I will compare the strengths and weaknesses of various frameworks.
Here, we have a simple neural network described in my slides about neural networks... It is using simple concepts from linear algebra to encapsulate the complexities (This makes possible to even use parallel matrix multiplication and some other algorithms to make everything faster) and making everything more modular and compact.
The data sets are coming from http://yann.lecun.com/exdb/mnist/.
beyond tellerrand: Mobile Apps with JavaScript – There's More Than WebHeiko Behrens
abstract from http://2011.beyondtellerrand.com
Modern web technologies and responsive design aim at a platform independent code base while promising first-class experience on any mobile device. Even though purely web-based approaches can achieve stunning results, they (still) cannot compete with their native counterpart regarding platform features and integration.
In this talk, I will show you how we can use JavaScript to produce mobile apps that include features such as native UI, push notifications, sensors, and paid distribution. You can expect lots of live demos when I will compare the strengths and weaknesses of various frameworks.
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.
This talk is a "how I did it" talk about how I took an idea, a web cam, Python, Django, and the Python Imaging Library and created art, explored science, and illustrated concepts that our ancestors knew by watching the sky but we have lost.
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
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.
Airline reservation project using JAVA in NetBeans IDEHimanshiSingh71
This project is based on the database connectivity JDBC . In this application user can book airline flight . This is offline airline program in which the user can book and cancelled their flight and payment through credit and debit card .
User can take the information of different types of airline flight . In this application user can also see and take knowledge about the famous historical places without moving to another website .
This application is also secure from hacker.
Overall , this application is very much useful for users because they can do offline flight booking and it is secure application for booking flight and other purposes.
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.
This talk is a "how I did it" talk about how I took an idea, a web cam, Python, Django, and the Python Imaging Library and created art, explored science, and illustrated concepts that our ancestors knew by watching the sky but we have lost.
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
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.
Airline reservation project using JAVA in NetBeans IDEHimanshiSingh71
This project is based on the database connectivity JDBC . In this application user can book airline flight . This is offline airline program in which the user can book and cancelled their flight and payment through credit and debit card .
User can take the information of different types of airline flight . In this application user can also see and take knowledge about the famous historical places without moving to another website .
This application is also secure from hacker.
Overall , this application is very much useful for users because they can do offline flight booking and it is secure application for booking flight and other purposes.
이 슬라이드는 Python과 node.js기반 데이터 분석 및 2D/3D 가시화 도구 및 코딩 방법을 알려주는 내용을 담고 있습니다. 엑셀처럼 데이터 계산 분석하고 싶거나, 수천개 데이터파일을 자동처리한 후, 가시화하고 싶거나, 3D그래픽으로 웹서버 형식 서비스하고 싶을 때 필요한 도구 사용법을 포함하고 있습니다. 데이터 분석, 가시화에 관심있는 분들을 위해, 오픈소스 도구들이 무엇이 있고, 어떻게 설치하고, 사용하는 지 간략히 정리되어 있으니 참고 바랍니다.
예제 소스 코드. https://github.com/mac999/visualize_data_sample
My JSConf.eu talk about next-gen JavaScript metaprogramming features, starting with ES5's new Object APIs and then focusing on the forthcoming Proxy object, approved for the next ECMA-262 Edition. This is beautiful work from Tom Van Cutsem and Mark Miller, with Andreas Gal helping on the implementation front -- proxies are already shipping in Firefox 4 betas.
Monads and Monoids: from daily java to Big Data analytics in Scala
Finally, after two decades of evolution, Java 8 made a step towards functional programming. What can Java learn from other mature functional languages? How to leverage obscure mathematical abstractions such as Monad or Monoid in practice? Usually people find it scary and difficult to understand. Oleksiy will explain these concepts in simple words to give a feeling of powerful tool applicable in many domains, from daily Java and Scala routines to Big Data analytics with Storm or Hadoop.
PLOTCON NYC: PlotlyJS.jl: Interactive plotting in JuliaPlotly
[PlotlyJS.jl](https://github.com/spencerlyon2/PlotlyJS.jl) leverages the unique features of Julia to bring the power of plotly.js to Julia users. PlotlyJS.jl has two main goals (1) Make it convenient to construct and manipulate _any_ plotly.js visualization from Julia and (2) provide infrastructure for viewing plots on multiple frontends and saving _publication quality plotly graphics_ to files. This talk will survey the capabilities of PlotlyJS.jl. Along the way we’ll pick up some Julia know-how and show some of the novel features PlotlyJS.jl brings to the plotly world.
Not Really Engineering, Barely a ScienceRod Begbie
Slide deck for a presentation I gave to teenagers at the National Student Leadership Conference in Berkley CA, to convince them that software engineering is the awesomest profession there is.
This is a talk I gave at the NYC Python Meetup group in October of 2010. I summarized the "big-picture" of NumPy and provided a draft version of the "Zen of NumPy"
Scientific Computing with Python Webinar March 19: 3D Visualization with MayaviEnthought, Inc.
In this webinar, Didrik Pinte provides an introduction to MayaVi, the 3D interactive visualization library for the open source Enthought Tool Suite. These tools provide scientists and engineers a sophisticated Python development framework for analysis and visualization.
February EPD Webinar: How do I...use PiCloud for cloud computing?Enthought, Inc.
In this Enthought Python Distribution Webinar, Ken Elkabany, co-founder of PiCloud, shows us how to run scientific and numeric Python code remotely on Amazon EC2. Through a partnership with Enthought, PiCloud now hosts EPD on it's cloud servers, allowing all EPD users to run their code remotely with ease. Several demonstrations are provided. For example code, visit http://enthought.com/training/webinars.php.
Scientific Computing with Python Webinar 9/18/2009:Curve FittingEnthought, Inc.
This webinar will provide an overview of the tools that SciPy and NumPy provide for regression analysis including linear and non-linear least-squares and a brief look at handling other error metrics. We will also demonstrate simple GUI tools that can make some problems easier and provide a quick overview of the new Scikits package statsmodels whose API is maturing in a separate package but should be incorporated into SciPy in the future.
Scientific Computing with Python Webinar --- August 28, 2009Enthought, Inc.
This month's webinar was a wrap-up of the SciPy 2009 conference. A treat for everyone who missed it! The recording of the webinar is available at www.enthought.com/training/SCPwebinar.php
5. Enthought Python Distribution (EPD)
Selections from our training courses including:
explanations, demonstrations, and tips
For subscribers to Enthought Python Distribution
(EPD)
Offered once a month for 60-90 minutes
depending on questions
Monday, February 22, 2010
6. Enthought Training Courses
Python Basics, NumPy,
SciPy, Matplotlib, Traits,
TraitsUI, Chaco…
Monday, February 22, 2010
7. Upcoming Training Classes
September 21 – 25, 2009
Introduction to Scientific Computing with Python
Austin, Texas
October 19 – 22, 2009
Python for Science, Eng., and Financial Analysis
Silicon Valley, California
November 9 – 12, 2009
Python for Science, Eng., and Financial Analysis
Chicago, Illinois
December 7 – 11, 2009
Introduction to Scientific Computing with Python
Austin, Texas
http://www.enthought.com/training/
Monday, February 22, 2010
12. Introduction
• Chaco is a plotting application toolkit
• You can build simple, static plots
Monday, February 22, 2010
13. Introduction
• Chaco is a plotting application toolkit
• You can build simple, static plots
• You can also build rich, interactive
visualizations:
Monday, February 22, 2010
14. “Script-oriented” Plotting
• from numpy import *
• from enthought.chaco.shell import *
• x = linspace(-2*pi, 2*pi, 100)
• y = sin(x)
• plot(x, y, 'r-')
• title('First plot')
• ytitle('sin(x)')
• show()
Monday, February 22, 2010
34. Tool Chooser
from enthought.traits.ui.api import CheckListEditor
class ToolsExample(HasTraits):
plot = Instance(Plot)
tools = List(editor=CheckListEditor(values = ["PanTool",
"SimpleZoom", "DragZoom"]))
Monday, February 22, 2010
35. Tool Chooser
from enthought.traits.ui.api import CheckListEditor
class ToolsExample(HasTraits):
plot = Instance(Plot)
tools = List(editor=CheckListEditor(values = ["PanTool",
"SimpleZoom", "DragZoom"]))
def __init__(self):
x = linspace(-14, 14, 500)
y = sin(x) * x**3
plotdata = ArrayPlotData(x = x, y = y)
plot = Plot(plotdata)
plot.plot(("x", "y"), type="line", color="blue")
plot.tools.append(PanTool(plot))
plot.tools.append(ZoomTool(plot))
plot.tools.append(DragZoom(plot, drag_button="right"))
self.plot = plot
Monday, February 22, 2010
36. Tool Chooser
def _tools_changed(self):
classes = [eval(class_name) for class_name in self.tools]
for tool in self.plot.tools:
if tool.__class__ not in classes:
self.plot.tools.remove(tool)
else:
classes.remove(tool.__class__)
for cls in classes:
self.plot.tools.append(cls(self.plot))
return
Monday, February 22, 2010
37. Tool Chooser
def _tools_changed(self):
classes = [eval(class_name) for class_name in self.tools]
for tool in self.plot.tools:
if tool.__class__ not in classes:
self.plot.tools.remove(tool)
else:
classes.remove(tool.__class__)
for cls in classes:
self.plot.tools.append(cls(self.plot))
return
Monday, February 22, 2010
38. Tool Chooser
def _tools_changed(self):
classes = [eval(class_name) for class_name in self.tools]
for tool in self.plot.tools:
if tool.__class__ not in classes:
self.plot.tools.remove(tool)
else:
classes.remove(tool.__class__)
for cls in classes:
self.plot.tools.append(cls(self.plot))
return
Monday, February 22, 2010
39. Tool Chooser
def _tools_changed(self):
classes = [eval(class_name) for class_name in self.tools]
for tool in self.plot.tools:
if tool.__class__ not in classes:
self.plot.tools.remove(tool)
else:
classes.remove(tool.__class__)
for cls in classes:
self.plot.tools.append(cls(self.plot))
return
Monday, February 22, 2010
40. Tool Chooser
def _tools_changed(self):
classes = [eval(class_name) for class_name in self.tools]
for tool in self.plot.tools:
if tool.__class__ not in classes:
self.plot.tools.remove(tool)
else:
classes.remove(tool.__class__)
for cls in classes:
self.plot.tools.append(cls(self.plot))
return
Monday, February 22, 2010
41. Tool Chooser
def _tools_changed(self):
classes = [eval(class_name) for class_name in self.tools]
for tool in self.plot.tools:
if tool.__class__ not in classes:
self.plot.tools.remove(tool)
else:
classes.remove(tool.__class__)
for cls in classes:
self.plot.tools.append(cls(self.plot))
return
Monday, February 22, 2010
42. Tool Chooser
def _tools_changed(self):
classes = [eval(class_name) for class_name in self.tools]
for tool in self.plot.tools:
if tool.__class__ not in classes:
self.plot.tools.remove(tool)
else:
classes.remove(tool.__class__)
for cls in classes:
self.plot.tools.append(cls(self.plot))
return
Monday, February 22, 2010
43. Tool Chooser
class ToolChooserExample(HasTraits):
plot = Instance(Plot)
tools = List(editor=CheckListEditor(values = ["PanTool", "ZoomTool",
"DragZoom"]))
traits_view = View(Item("tools", label="Tools", style="custom"),
Item('plot', editor=ComponentEditor(), show_label=False),
width=800, height=600, resizable=True,
title="Tool Chooser")
def __init__(self):
...
def _tools_changed(self):
classes = [eval(class_name) for class_name in self.tools]
# Remove all tools that are not in the enabled list in self.tools
for tool in self.plot.tools:
if tool.__class__ not in classes:
self.plot.tools.remove(tool)
else:
classes.remove(tool.__class__)
# Create new instances of tools for the remaining tool classes
for cls in classes:
self.plot.tools.append(cls(self.plot))
return
Monday, February 22, 2010