Visual Speech Recognition Using a 3D Convolutional Neural Network
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Sagemath and Sagemathcloud
Viviane Pons Ma^ıtrede conf´erence,Universit´eParis-Sud Orsay [email protected] { @PyViv SageMath and SageMathCloud Introduction SageMath SageMath is a free open source mathematics software I Created in 2005 by William Stein. I http://www.sagemath.org/ I Mission: Creating a viable free open source alternative to Magma, Maple, Mathematica and Matlab. Viviane Pons (U-PSud) SageMath and SageMathCloud October 19, 2016 2 / 7 SageMath Source and language I the main language of Sage is python (but there are many other source languages: cython, C, C++, fortran) I the source is distributed under the GPL licence. Viviane Pons (U-PSud) SageMath and SageMathCloud October 19, 2016 3 / 7 SageMath Sage and libraries One of the original purpose of Sage was to put together the many existent open source mathematics software programs: Atlas, GAP, GMP, Linbox, Maxima, MPFR, PARI/GP, NetworkX, NTL, Numpy/Scipy, Singular, Symmetrica,... Sage is all-inclusive: it installs all those libraries and gives you a common python-based interface to work on them. On top of it is the python / cython Sage library it-self. Viviane Pons (U-PSud) SageMath and SageMathCloud October 19, 2016 4 / 7 SageMath Sage and libraries I You can use a library explicitly: sage: n = gap(20062006) sage: type(n) <c l a s s 'sage. interfaces .gap.GapElement'> sage: n.Factors() [ 2, 17, 59, 73, 137 ] I But also, many of Sage computation are done through those libraries without necessarily telling you: sage: G = PermutationGroup([[(1,2,3),(4,5)],[(3,4)]]) sage : G . g a p () Group( [ (3,4), (1,2,3)(4,5) ] ) Viviane Pons (U-PSud) SageMath and SageMathCloud October 19, 2016 5 / 7 SageMath Development model Development model I Sage is developed by researchers for researchers: the original philosophy is to develop what you need for your research and share it with the community. -
Getting Started with Machine Learning
Getting Started with Machine Learning CSC131 The Beauty & Joy of Computing Cornell College 600 First Street SW Mount Vernon, Iowa 52314 September 2018 ii Contents 1 Applications: where machine learning is helping1 1.1 Sheldon Branch............................1 1.2 Bram Dedrick.............................4 1.3 Tony Ferenzi.............................5 1.3.1 Benefits of Machine Learning................5 1.4 William Golden............................7 1.4.1 Humans: The Teachers of Technology...........7 1.5 Yuan Hong..............................9 1.6 Easton Jensen............................. 11 1.7 Rodrigo Martinez........................... 13 1.7.1 Machine Learning in Medicine............... 13 1.8 Matt Morrical............................. 15 1.9 Ella Nelson.............................. 16 1.10 Koichi Okazaki............................ 17 1.11 Jakob Orel.............................. 19 1.12 Marcellus Parks............................ 20 1.13 Lydia Sanchez............................. 22 1.14 Tiff Serra-Pichardo.......................... 24 1.15 Austin Stala.............................. 25 1.16 Nicole Trenholm........................... 26 1.17 Maddy Weaver............................ 28 1.18 Peter Weber.............................. 29 iii iv CONTENTS 2 Recommendations: How to learn more about machine learning 31 2.1 Sheldon Branch............................ 31 2.1.1 Course 1: Machine Learning................. 31 2.1.2 Course 2: Robotics: Vision Intelligence and Machine Learn- ing............................... 33 2.1.3 Course -
How to Access Python for Doing Scientific Computing
How to access Python for doing scientific computing1 Hans Petter Langtangen1,2 1Center for Biomedical Computing, Simula Research Laboratory 2Department of Informatics, University of Oslo Mar 23, 2015 A comprehensive eco system for scientific computing with Python used to be quite a challenge to install on a computer, especially for newcomers. This problem is more or less solved today. There are several options for getting easy access to Python and the most important packages for scientific computations, so the biggest issue for a newcomer is to make a proper choice. An overview of the possibilities together with my own recommendations appears next. Contents 1 Required software2 2 Installing software on your laptop: Mac OS X and Windows3 3 Anaconda and Spyder4 3.1 Spyder on Mac............................4 3.2 Installation of additional packages.................5 3.3 Installing SciTools on Mac......................5 3.4 Installing SciTools on Windows...................5 4 VMWare Fusion virtual machine5 4.1 Installing Ubuntu...........................6 4.2 Installing software on Ubuntu....................7 4.3 File sharing..............................7 5 Dual boot on Windows8 6 Vagrant virtual machine9 1The material in this document is taken from a chapter in the book A Primer on Scientific Programming with Python, 4th edition, by the same author, published by Springer, 2014. 7 How to write and run a Python program9 7.1 The need for a text editor......................9 7.2 Spyder................................. 10 7.3 Text editors.............................. 10 7.4 Terminal windows.......................... 11 7.5 Using a plain text editor and a terminal window......... 12 8 The SageMathCloud and Wakari web services 12 8.1 Basic intro to SageMathCloud................... -
An Introduction to Numpy and Scipy
An introduction to Numpy and Scipy Table of contents Table of contents ............................................................................................................................ 1 Overview ......................................................................................................................................... 2 Installation ...................................................................................................................................... 2 Other resources .............................................................................................................................. 2 Importing the NumPy module ........................................................................................................ 2 Arrays .............................................................................................................................................. 3 Other ways to create arrays............................................................................................................ 7 Array mathematics .......................................................................................................................... 8 Array iteration ............................................................................................................................... 10 Basic array operations .................................................................................................................. 11 Comparison operators and value testing .................................................................................... -
CV, Dlib, Flask, Opencl, CUDA, Matlab/Octave, Assembly/Intrinsics, Cmake, Make, Git, Linux CLI
Arun Arun Ponnusamy Visakhapatnam, India. Ponnusamy Computer Vision [email protected] Research Engineer www.arunponnusamy.com github.com/arunponnusamy ㅡ Skills Passionate about Image Processing, Computer Vision and Machine Learning. Key skills - C/C++, Python, Keras,TensorFlow, PyTorch, OpenCV, dlib, Flask, OpenCL, CUDA, Matlab/Octave, Assembly/Intrinsics, CMake, Make, Git, Linux CLI. ㅡ Experience care.ai (5 years) Computer Vision Research Engineer JAN 2019 - PRESENT, VISAKHAPATNAM Key areas worked on - image classification, object detection, action recognition, face detection and face recognition for edge devices. Tools and technologies - TensorFlow/Keras, TensorFlow Lite, TensorRT, OpenCV, dlib, Python, Google Cloud. Devices - Nvidia Jetson Nano / TX2, Google Coral Edge TPU. 1000Lookz Senior Computer Vision Engineer JAN 2018 - JAN 2019, CHENNAI Key areas worked on - head pose estimation, face analysis, image classification and object detection. Tools and technologies - TensorFlow/Keras, OpenCV, dlib, Flask, Python, AWS, Google Cloud. MulticoreWare Inc. Software Engineer JULY 2014 - DEC 2017, CHENNAI Key areas worked on - image processing, computer vision, parallel processing, software optimization and porting. Tools and technologies - OpenCV, dlib, C/C++, OpenCL, CUDA. ㅡ PSG College of Technology / Bachelor of Engineering Education JULY 2010 - MAY 2014, COIMBATORE Bachelor’s degree in Electronics and Communication Engineering with CGPA of 8.42 (out of 10). Favourite subjects - Digital Electronics, Embedded Systems and C Programming. ㅡ Notable Served as one of the Joint Secretaries of Institution of Engineers(I) (students’ chapter) of PSG College of Technology. Efforts Secured first place in Line Tracer Event in Kriya ‘13, a Techno management fest conducted by Students Union of PSG College of Technology. Actively participated in FIRST Tech Challenge, a robotics competition, conducted by Caterpillar India. -
Homework 2 an Introduction to Convolutional Neural Networks
Homework 2 An Introduction to Convolutional Neural Networks 11-785: Introduction to Deep Learning (Spring 2019) OUT: February 17, 2019 DUE: March 10, 2019, 11:59 PM Start Here • Collaboration policy: { You are expected to comply with the University Policy on Academic Integrity and Plagiarism. { You are allowed to talk with / work with other students on homework assignments { You can share ideas but not code, you must submit your own code. All submitted code will be compared against all code submitted this semester and in previous semesters using MOSS. • Overview: { Part 1: All of the problems in Part 1 will be graded on Autolab. You can download the starter code from Autolab as well. This assignment has 100 points total. { Part 2: This section of the homework is an open ended competition hosted on Kaggle.com, a popular service for hosting predictive modeling and data analytic competitions. All of the details for completing Part 2 can be found on the competition page. • Submission: { Part 1: The compressed handout folder hosted on Autolab contains four python files, layers.py, cnn.py, mlp.py and local grader.py, and some folders data, autograde and weights. File layers.py contains common layers in convolutional neural networks. File cnn.py contains outline class of two convolutional neural networks. File mlp.py contains a basic MLP network. File local grader.py is foryou to evaluate your own code before submitting online. Folders contain some data to be used for section 1.3 and 1.4 of this homework. Make sure that all class and function definitions originally specified (as well as class attributes and methods) in the two files layers.py and cnn.py are fully implemented to the specification provided in this write-up. -
User Guide Release 1.18.4
NumPy User Guide Release 1.18.4 Written by the NumPy community May 24, 2020 CONTENTS 1 Setting up 3 2 Quickstart tutorial 9 3 NumPy basics 33 4 Miscellaneous 97 5 NumPy for Matlab users 103 6 Building from source 111 7 Using NumPy C-API 115 Python Module Index 163 Index 165 i ii NumPy User Guide, Release 1.18.4 This guide is intended as an introductory overview of NumPy and explains how to install and make use of the most important features of NumPy. For detailed reference documentation of the functions and classes contained in the package, see the reference. CONTENTS 1 NumPy User Guide, Release 1.18.4 2 CONTENTS CHAPTER ONE SETTING UP 1.1 What is NumPy? NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidi- mensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more. At the core of the NumPy package, is the ndarray object. This encapsulates n-dimensional arrays of homogeneous data types, with many operations being performed in compiled code for performance. There are several important differences between NumPy arrays and the standard Python sequences: • NumPy arrays have a fixed size at creation, unlike Python lists (which can grow dynamically). Changing the size of an ndarray will create a new array and delete the original. -
Introduction to Python in HPC
Introduction to Python in HPC Omar Padron National Center for Supercomputing Applications University of Illinois at Urbana Champaign [email protected] Outline • What is Python? • Why Python for HPC? • Python on Blue Waters • How to load it • Available Modules • Known Limitations • Where to Get More information 2 What is Python? • Modern programming language with a succinct and intuitive syntax • Has grown over the years to offer an extensive software ecosystem • Interprets JIT-compiled byte-code, but can take full advantage of AOT-compiled code • using python libraries with AOT code (numpy, scipy) • interfacing seamlessly with AOT code (CPython API) • generating native modules with/from AOT code (cython) 3 Why Python for HPC? • When you want to maximize productivity (not necessarily performance) • Mature language with large user base • Huge collection of freely available software libraries • High Performance Computing • Engineering, Optimization, Differential Equations • Scientific Datasets, Analysis, Visualization • General-purpose computing • web apps, GUIs, databases, and tons more • Python combines the best of both JIT and AOT code. • Write performance critical loops and kernels in C/FORTRAN • Write high level logic and “boiler plate” in Python 4 Python on Blue Waters • Blue Waters Python Software Stack (bw-python) • Over 60 python modules supporting HPC applications • Implements the Scipy Stack Specification (http://www.scipy.org/stackspec.html) • Provides extensions for parallel programming (mpi4py, pycuda) and scientific data IO (h5py, pycuda) • Numerical routines linked from ACML • Built specifically for running jobs on the compute nodes module swap PrgEnv-cray PrgEnv-gnu module load bw-python python ! Python 2.7.5 (default, Jun 2 2014, 02:41:21) [GCC 4.8.2 20131016 (Cray Inc.)] on Blue Waters Type "help", "copyright", "credits" or "license" for more information. -
Sage 9.4 Reference Manual: Coercion Release 9.4
Sage 9.4 Reference Manual: Coercion Release 9.4 The Sage Development Team Aug 24, 2021 CONTENTS 1 Preliminaries 1 1.1 What is coercion all about?.......................................1 1.2 Parents and Elements...........................................2 1.3 Maps between Parents..........................................3 2 Basic Arithmetic Rules 5 3 How to Implement 9 3.1 Methods to implement..........................................9 3.2 Example................................................. 10 3.3 Provided Methods............................................ 13 4 Discovering new parents 15 5 Modules 17 5.1 The coercion model........................................... 17 5.2 Coerce actions.............................................. 36 5.3 Coerce maps............................................... 39 5.4 Coercion via construction functors.................................... 40 5.5 Group, ring, etc. actions on objects................................... 68 5.6 Containers for storing coercion data................................... 71 5.7 Exceptions raised by the coercion model................................ 78 6 Indices and Tables 79 Python Module Index 81 Index 83 i ii CHAPTER ONE PRELIMINARIES 1.1 What is coercion all about? The primary goal of coercion is to be able to transparently do arithmetic, comparisons, etc. between elements of distinct sets. As a concrete example, when one writes 1 + 1=2 one wants to perform arithmetic on the operands as rational numbers, despite the left being an integer. This makes sense given the obvious -
Pandas: Powerful Python Data Analysis Toolkit Release 0.25.3
pandas: powerful Python data analysis toolkit Release 0.25.3 Wes McKinney& PyData Development Team Nov 02, 2019 CONTENTS i ii pandas: powerful Python data analysis toolkit, Release 0.25.3 Date: Nov 02, 2019 Version: 0.25.3 Download documentation: PDF Version | Zipped HTML Useful links: Binary Installers | Source Repository | Issues & Ideas | Q&A Support | Mailing List pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. See the overview for more detail about whats in the library. CONTENTS 1 pandas: powerful Python data analysis toolkit, Release 0.25.3 2 CONTENTS CHAPTER ONE WHATS NEW IN 0.25.2 (OCTOBER 15, 2019) These are the changes in pandas 0.25.2. See release for a full changelog including other versions of pandas. Note: Pandas 0.25.2 adds compatibility for Python 3.8 (GH28147). 1.1 Bug fixes 1.1.1 Indexing • Fix regression in DataFrame.reindex() not following the limit argument (GH28631). • Fix regression in RangeIndex.get_indexer() for decreasing RangeIndex where target values may be improperly identified as missing/present (GH28678) 1.1.2 I/O • Fix regression in notebook display where <th> tags were missing for DataFrame.index values (GH28204). • Regression in to_csv() where writing a Series or DataFrame indexed by an IntervalIndex would incorrectly raise a TypeError (GH28210) • Fix to_csv() with ExtensionArray with list-like values (GH28840). 1.1.3 Groupby/resample/rolling • Bug incorrectly raising an IndexError when passing a list of quantiles to pandas.core.groupby. DataFrameGroupBy.quantile() (GH28113). -
ML Cheatsheet Documentation
ML Cheatsheet Documentation Team Sep 02, 2021 Basics 1 Linear Regression 3 2 Gradient Descent 21 3 Logistic Regression 25 4 Glossary 39 5 Calculus 45 6 Linear Algebra 57 7 Probability 67 8 Statistics 69 9 Notation 71 10 Concepts 75 11 Forwardpropagation 81 12 Backpropagation 91 13 Activation Functions 97 14 Layers 105 15 Loss Functions 117 16 Optimizers 121 17 Regularization 127 18 Architectures 137 19 Classification Algorithms 151 20 Clustering Algorithms 157 i 21 Regression Algorithms 159 22 Reinforcement Learning 161 23 Datasets 165 24 Libraries 181 25 Papers 211 26 Other Content 217 27 Contribute 223 ii ML Cheatsheet Documentation Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more. Warning: This document is under early stage development. If you find errors, please raise an issue or contribute a better definition! Basics 1 ML Cheatsheet Documentation 2 Basics CHAPTER 1 Linear Regression • Introduction • Simple regression – Making predictions – Cost function – Gradient descent – Training – Model evaluation – Summary • Multivariable regression – Growing complexity – Normalization – Making predictions – Initialize weights – Cost function – Gradient descent – Simplifying with matrices – Bias term – Model evaluation 3 ML Cheatsheet Documentation 1.1 Introduction Linear Regression is a supervised machine learning algorithm where the predicted output is continuous and has a constant slope. It’s used to predict values within a continuous range, (e.g. sales, price) rather than trying to classify them into categories (e.g. cat, dog). There are two main types: Simple regression Simple linear regression uses traditional slope-intercept form, where m and b are the variables our algorithm will try to “learn” to produce the most accurate predictions. -
MATLAB Commands in Numerical Python (Numpy) 1 Vidar Bronken Gundersen /Mathesaurus.Sf.Net
MATLAB commands in numerical Python (NumPy) 1 Vidar Bronken Gundersen /mathesaurus.sf.net MATLAB commands in numerical Python (NumPy) Copyright c Vidar Bronken Gundersen Permission is granted to copy, distribute and/or modify this document as long as the above attribution is kept and the resulting work is distributed under a license identical to this one. The idea of this document (and the corresponding xml instance) is to provide a quick reference for switching from matlab to an open-source environment, such as Python, Scilab, Octave and Gnuplot, or R for numeric processing and data visualisation. Where Octave and Scilab commands are omitted, expect Matlab compatibility, and similarly where non given use the generic command. Time-stamp: --T:: vidar 1 Help Desc. matlab/Octave Python R Browse help interactively doc help() help.start() Octave: help -i % browse with Info Help on using help help help or doc doc help help() Help for a function help plot help(plot) or ?plot help(plot) or ?plot Help for a toolbox/library package help splines or doc splines help(pylab) help(package=’splines’) Demonstration examples demo demo() Example using a function example(plot) 1.1 Searching available documentation Desc. matlab/Octave Python R Search help files lookfor plot help.search(’plot’) Find objects by partial name apropos(’plot’) List available packages help help(); modules [Numeric] library() Locate functions which plot help(plot) find(plot) List available methods for a function methods(plot) 1.2 Using interactively Desc. matlab/Octave Python R Start session Octave: octave -q ipython -pylab Rgui Auto completion Octave: TAB or M-? TAB Run code from file foo(.m) execfile(’foo.py’) or run foo.py source(’foo.R’) Command history Octave: history hist -n history() Save command history diary on [..] diary off savehistory(file=".Rhistory") End session exit or quit CTRL-D q(save=’no’) CTRL-Z # windows sys.exit() 2 Operators Desc.