We first present the Python programming language and the NumPy package for scientific computing. Then, we devise a digit recognition system highlighting the scikit-learn package.
This is the slides for the data science workshop at CDIPS, UC Berkeley on 06-28-2017. It is about general machine learning with a focus on scikit-learn. You can find all the related material: https://github.com/qingkaikong/20170628_ML_sklearn
Machine learning in production with scikit-learnJeff Klukas
Presented at PyOhio 2017: https://pyohio.org/schedule/presentation/284/
The Python data ecosystem provides amazing tools to quickly get up and running with machine learning models, but the path to stably serving them in production is not so clear. We'll discuss details of wrapping a minimal REST API around scikit-learn, training and persisting models in batch, and logging decisions, then compare to some other common approaches to productionizing models.
19. Java data structures algorithms and complexityIntro C# Book
In this chapter we will compare the data structures we have learned so far by the performance (execution speed) of the basic operations (addition, search, deletion, etc.). We will give specific tips in what situations what data structures to use.
Abstract: This PDSG workshop introduces basic concepts on TensorFlow. The course covers fundamentals. Concepts covered are Vectors/Matrices/Vectors, Design&Run, Constants, Operations, Placeholders, Bindings, Operators, Loss Function and Training.
Level: Fundamental
Requirements: Some basic programming knowledge is preferred. No prior statistics background is required.
Rajat Monga at AI Frontiers: Deep Learning with TensorFlowAI Frontiers
In this talk at AI Frontiers Conference, Rajat Monga shares about TensorFlow that has enabled cutting-edge machine learning research at the top AI labs in the world. At the same time it has made the technology accessible to a large audience leading to some amazing uses. TensorFlow is used for classification, recommendation, text parsing, sentiment analysis and more. This talk goes over the design that makes it fast, flexible, and easy to use, and describe how we continue to make it better.
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding and recommendation engines. One of the key reasons for this progress is the availability of highly flexible and developer friendly deep learning frameworks. During this workshop, members of the Amazon Machine Learning team will provide a short background on Deep Learning focusing on relevant application domains and an introduction to using the powerful and scalable Deep Learning framework, MXNet. At the end of this tutorial you’ll gain hands on experience targeting a variety of applications including computer vision and recommendation engines as well as exposure to how to use preconfigured Deep Learning AMIs and CloudFormation Templates to help speed your development.
Abstract: This workshop teaches basic algorithms in whiteboarding interviews. All the code examples are in Python and the course has dual purpose teaching basic Python programming.
This is the slides for the data science workshop at CDIPS, UC Berkeley on 06-28-2017. It is about general machine learning with a focus on scikit-learn. You can find all the related material: https://github.com/qingkaikong/20170628_ML_sklearn
Machine learning in production with scikit-learnJeff Klukas
Presented at PyOhio 2017: https://pyohio.org/schedule/presentation/284/
The Python data ecosystem provides amazing tools to quickly get up and running with machine learning models, but the path to stably serving them in production is not so clear. We'll discuss details of wrapping a minimal REST API around scikit-learn, training and persisting models in batch, and logging decisions, then compare to some other common approaches to productionizing models.
19. Java data structures algorithms and complexityIntro C# Book
In this chapter we will compare the data structures we have learned so far by the performance (execution speed) of the basic operations (addition, search, deletion, etc.). We will give specific tips in what situations what data structures to use.
Abstract: This PDSG workshop introduces basic concepts on TensorFlow. The course covers fundamentals. Concepts covered are Vectors/Matrices/Vectors, Design&Run, Constants, Operations, Placeholders, Bindings, Operators, Loss Function and Training.
Level: Fundamental
Requirements: Some basic programming knowledge is preferred. No prior statistics background is required.
Rajat Monga at AI Frontiers: Deep Learning with TensorFlowAI Frontiers
In this talk at AI Frontiers Conference, Rajat Monga shares about TensorFlow that has enabled cutting-edge machine learning research at the top AI labs in the world. At the same time it has made the technology accessible to a large audience leading to some amazing uses. TensorFlow is used for classification, recommendation, text parsing, sentiment analysis and more. This talk goes over the design that makes it fast, flexible, and easy to use, and describe how we continue to make it better.
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding and recommendation engines. One of the key reasons for this progress is the availability of highly flexible and developer friendly deep learning frameworks. During this workshop, members of the Amazon Machine Learning team will provide a short background on Deep Learning focusing on relevant application domains and an introduction to using the powerful and scalable Deep Learning framework, MXNet. At the end of this tutorial you’ll gain hands on experience targeting a variety of applications including computer vision and recommendation engines as well as exposure to how to use preconfigured Deep Learning AMIs and CloudFormation Templates to help speed your development.
Abstract: This workshop teaches basic algorithms in whiteboarding interviews. All the code examples are in Python and the course has dual purpose teaching basic Python programming.
Alex Smola at AI Frontiers: Scalable Deep Learning Using MXNetAI Frontiers
In this talk at AI Frontiers Conference, Alex Smola gives a brief overview over the features used to scale deep learning using MXNet. It relies on a mix between declarative and imperative programming to achieve efficiency while also allowing for significant flexibility for the user. It relies on a distributed (key, value) store for synchronization between GPUs and between machines. It also relies on the separation between a highly efficient execution engine and language bindings to achieve a high degree of flexibility between different languages while offering a native feel in each of them. Alex also briefly discusses how Amazon AWS can help deploy deep learning models and outline steps on our future roadmap.
Java Foundations: Data Types and Type ConversionSvetlin Nakov
Learn how to use data types and variables in Java, how variables are stored in the memory and how to convert from one data type to another.
Watch the video lesson and access the hands-on exercises here: https://softuni.org/code-lessons/java-foundations-certification-data-types-and-variables
Python- Creating Dictionary,
Accessing and Modifying key: value Pairs in Dictionaries
Built-In Functions used on Dictionaries,
Dictionary Methods
Removing items from dictionary
TensorFlow Tutorial | Deep Learning With TensorFlow | TensorFlow Tutorial For...Simplilearn
This presentation on TensorFlow will help you understand what is Deep Learning and it's libraries, why use TensorFlow, what is TensorFlow, how to build a computational graph, programming using elements in TensorFlow, what are Recurrent Neural Networks along with a use case implementation on TensorFlow. TensorFlow is a software library developed by Google for the purposes of conducting machine learning and deep neural network research. In this video, you will learn the fundamentals of TensorFlow concepts, functions and operations required to implement deep learning algorithms and leverage data like never before. Now let's get started in mastering the concept of Deep Learning using TensorFlow.
Below topics are explained in this TensorFlow presentation:
1. What is Deep Learning?
2. Top Deep Learning libraries?
3. Why use TensorFlow?
4. What is TensorFlow?
5. Building a computational graph
6. Programming elements in TensorFlow
7. Introducing Recurrent Neural Networks
8. Use case implementation of RNN using TensorFlow
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks. Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning and artificial intelligence
Learn more at: https://www.simplilearn.com
In this chapter we will learn about arrays as a way to work with sequences of elements of the same type. We will explain what arrays are, how we declare, create, instantiate and use them. We will examine one-dimensional and multidimensional arrays. We will learn different ways to iterate through the array, read from the standard input and write to the standard output. We will give many example exercises, which can be solved using arrays and we will show how useful they really are.
TensorFlow Tutorial | Deep Learning Using TensorFlow | TensorFlow Tutorial Py...Edureka!
This Edureka TensorFlow Tutorial (Blog: https://goo.gl/HTE7uB) will help you in understanding various important basics of TensorFlow. It also includes a use-case in which we will create a model that will differentiate between a rock and a mine using TensorFlow. Below are the topics covered in this tutorial:
1. What are Tensors?
2. What is TensorFlow?
3. TensorFlow Code-basics
4. Graph Visualization
5. TensorFlow Data structures
6. Use-Case Naval Mine Identifier (NMI)
Introduction To TensorFlow | Deep Learning Using TensorFlow | CloudxLabCloudxLab
( Machine Learning & Deep Learning Specialization Training: https://goo.gl/6n3vko )
This CloudxLab TensorFlow tutorial helps you to understand TensorFlow in detail. Below are the topics covered in this tutorial:
1) Why TensorFlow?
2) What are Tensors?
3) What is TensorFlow?
4) Creating your First Graph
5) Linear Regression with TensorFlow
6) Implementing Gradient Descent using TensorFlow
7) Implementing Gradient Descent Using autodiff
8) Implementing Gradient Descent Using an Optimizer
9) Graph Visualization using TensorBoard
10) Name Scopes in TensorFlow
11) Modularity in TensorFlow
12) Sharing Variables in TensorFlow
Learn how to use arrays in Java, how to enter array, how to traverse an array, how to print array and more array operations.
Watch the video lesson and access the hands-on exercises here: https://softuni.org/code-lessons/java-foundations-certification-arrays
In this chapter we will analyze more complex data structures like dictionaries and sets, and their implementations with hash-tables and balanced trees. We will explain in more details what hashing and hash-tables mean and why they are such an important part of programming. We will discuss the concept of "collisions" and how they might happen when implementing hash-tables. Also we will offer you different types of approaches for solving this type of issues. We will look at the abstract data structure set and explain how it can be implemented with the ADTs dictionary and balanced search tree. Also we will provide you with examples that illustrate the behavior of these data structures with real world examples.
In this chapter we are going to get familiar with some of the basic presentations of data in programming: lists and linear data structures. Very often in order to solve a given problem we need to work with a sequence of elements. For example, to read completely this book we have to read sequentially each page, i.e. to traverse sequentially each of the elements of the set of the pages in the book. Depending on the task, we have to apply different operations on this set of data. In this chapter we will introduce the concept of abstract data types (ADT) and will explain how a certain ADT can have multiple different implementations. After that we shall explore how and when to use lists and their implementations (linked list, doubly-linked list and array-list). We are going to see how for a given task one structure may be more convenient than another. We are going to consider the structures "stack" and "queue", as well as their applications. We are going to get familiar with some implementations of these structures.
Alex Smola at AI Frontiers: Scalable Deep Learning Using MXNetAI Frontiers
In this talk at AI Frontiers Conference, Alex Smola gives a brief overview over the features used to scale deep learning using MXNet. It relies on a mix between declarative and imperative programming to achieve efficiency while also allowing for significant flexibility for the user. It relies on a distributed (key, value) store for synchronization between GPUs and between machines. It also relies on the separation between a highly efficient execution engine and language bindings to achieve a high degree of flexibility between different languages while offering a native feel in each of them. Alex also briefly discusses how Amazon AWS can help deploy deep learning models and outline steps on our future roadmap.
Java Foundations: Data Types and Type ConversionSvetlin Nakov
Learn how to use data types and variables in Java, how variables are stored in the memory and how to convert from one data type to another.
Watch the video lesson and access the hands-on exercises here: https://softuni.org/code-lessons/java-foundations-certification-data-types-and-variables
Python- Creating Dictionary,
Accessing and Modifying key: value Pairs in Dictionaries
Built-In Functions used on Dictionaries,
Dictionary Methods
Removing items from dictionary
TensorFlow Tutorial | Deep Learning With TensorFlow | TensorFlow Tutorial For...Simplilearn
This presentation on TensorFlow will help you understand what is Deep Learning and it's libraries, why use TensorFlow, what is TensorFlow, how to build a computational graph, programming using elements in TensorFlow, what are Recurrent Neural Networks along with a use case implementation on TensorFlow. TensorFlow is a software library developed by Google for the purposes of conducting machine learning and deep neural network research. In this video, you will learn the fundamentals of TensorFlow concepts, functions and operations required to implement deep learning algorithms and leverage data like never before. Now let's get started in mastering the concept of Deep Learning using TensorFlow.
Below topics are explained in this TensorFlow presentation:
1. What is Deep Learning?
2. Top Deep Learning libraries?
3. Why use TensorFlow?
4. What is TensorFlow?
5. Building a computational graph
6. Programming elements in TensorFlow
7. Introducing Recurrent Neural Networks
8. Use case implementation of RNN using TensorFlow
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks. Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning and artificial intelligence
Learn more at: https://www.simplilearn.com
In this chapter we will learn about arrays as a way to work with sequences of elements of the same type. We will explain what arrays are, how we declare, create, instantiate and use them. We will examine one-dimensional and multidimensional arrays. We will learn different ways to iterate through the array, read from the standard input and write to the standard output. We will give many example exercises, which can be solved using arrays and we will show how useful they really are.
TensorFlow Tutorial | Deep Learning Using TensorFlow | TensorFlow Tutorial Py...Edureka!
This Edureka TensorFlow Tutorial (Blog: https://goo.gl/HTE7uB) will help you in understanding various important basics of TensorFlow. It also includes a use-case in which we will create a model that will differentiate between a rock and a mine using TensorFlow. Below are the topics covered in this tutorial:
1. What are Tensors?
2. What is TensorFlow?
3. TensorFlow Code-basics
4. Graph Visualization
5. TensorFlow Data structures
6. Use-Case Naval Mine Identifier (NMI)
Introduction To TensorFlow | Deep Learning Using TensorFlow | CloudxLabCloudxLab
( Machine Learning & Deep Learning Specialization Training: https://goo.gl/6n3vko )
This CloudxLab TensorFlow tutorial helps you to understand TensorFlow in detail. Below are the topics covered in this tutorial:
1) Why TensorFlow?
2) What are Tensors?
3) What is TensorFlow?
4) Creating your First Graph
5) Linear Regression with TensorFlow
6) Implementing Gradient Descent using TensorFlow
7) Implementing Gradient Descent Using autodiff
8) Implementing Gradient Descent Using an Optimizer
9) Graph Visualization using TensorBoard
10) Name Scopes in TensorFlow
11) Modularity in TensorFlow
12) Sharing Variables in TensorFlow
Learn how to use arrays in Java, how to enter array, how to traverse an array, how to print array and more array operations.
Watch the video lesson and access the hands-on exercises here: https://softuni.org/code-lessons/java-foundations-certification-arrays
In this chapter we will analyze more complex data structures like dictionaries and sets, and their implementations with hash-tables and balanced trees. We will explain in more details what hashing and hash-tables mean and why they are such an important part of programming. We will discuss the concept of "collisions" and how they might happen when implementing hash-tables. Also we will offer you different types of approaches for solving this type of issues. We will look at the abstract data structure set and explain how it can be implemented with the ADTs dictionary and balanced search tree. Also we will provide you with examples that illustrate the behavior of these data structures with real world examples.
In this chapter we are going to get familiar with some of the basic presentations of data in programming: lists and linear data structures. Very often in order to solve a given problem we need to work with a sequence of elements. For example, to read completely this book we have to read sequentially each page, i.e. to traverse sequentially each of the elements of the set of the pages in the book. Depending on the task, we have to apply different operations on this set of data. In this chapter we will introduce the concept of abstract data types (ADT) and will explain how a certain ADT can have multiple different implementations. After that we shall explore how and when to use lists and their implementations (linked list, doubly-linked list and array-list). We are going to see how for a given task one structure may be more convenient than another. We are going to consider the structures "stack" and "queue", as well as their applications. We are going to get familiar with some implementations of these structures.
Personal point of view on scikit-learn: past, present, and future.
This talks gives a bit of history, mentions exciting development, and a personal vision on the future.
Tutorial on Scikit Learn I gave at SF Data Mining meetup on May 1st 2017. Review of major parts of the Scikit-Learn API and quick coding exercise on Iris Dataset
Data Science and Machine Learning Using Python and Scikit-learnAsim Jalis
Workshop at DataEngConf 2016, on April 7-8 2016, at Galvanize, 44 Tehama Street, San Francisco, CA.
Demo and labs for workshop are at https://github.com/asimjalis/data-science-workshop
Tree models with Scikit-Learn: Great models with little assumptionsGilles Louppe
This talk gives an introduction to tree-based methods, both from a theoretical and practical point of view. It covers decision trees, random forests and boosting estimators, along with concrete examples based on Scikit-Learn about how they work, when they work and why they work.
Realtime predictive analytics using RabbitMQ & scikit-learnAWeber
In this talk, AWeber's Michael Becker describes how to deploy a predictive model in a production environment using RabbitMQ and scikit-learn. You'll see a realtime content classification system to demonstrate this design.
A brief introduction to clustering with Scikit learn. In this presentation, we provide an overview with real examples of how to make use and optimize within k-means clustering.
Introduction to Machine Learning with Python and scikit-learnMatt Hagy
PyATL talk about machine learning. Provides both an intro to machine learning and how to do it with Python. Includes simple examples with code and results.
scikit-learn has emerged as one of the most popular open source machine learning toolkits, now widely used in academia and industry.
scikit-learn provides easy-to-use interfaces to perform advanced analysis and build powerful predictive models.
The tutorial will cover basic concepts of machine learning, such as supervised and unsupervised learning, cross validation, and model selection. We will see how to prepare data for machine learning, and go from applying a single algorithm to building a machine learning pipeline.
We will also cover how to build machine learning models on text data, and how to handle very large datasets.
In this talk by AWeber's Michael Becker, you will get a brief overview of Machine Learning and scikit-learn. This is a scaled down version of this talk from Pycon 2013: http://github.com/jakevdp/sklearn_pycon2013
Scikit-learn for easy machine learning: the vision, the tool, and the projectGael Varoquaux
Scikit-learn is a popular machine learning tool. What can it do for you?Why you you want to use it? What can you do with it? Where is it going?In this talk, I will discuss why and how scikit-learn became popular. Iwill argue that it is successful because of its vision: it fills an important slot in the rich ecosystem of data science. I will demonstrate how scikit-learn makes predictive analysis easy and yet versatile.I will shed some light on our development process: how do we, as a community, ensure the quality and the growth of scikit-learn?
Accelerating Random Forests in Scikit-LearnGilles Louppe
Random Forests are without contest one of the most robust, accurate and versatile tools for solving machine learning tasks. Implementing this algorithm properly and efficiently remains however a challenging task involving issues that are easily overlooked if not considered with care. In this talk, we present the Random Forests implementation developed within the Scikit-Learn machine learning library. In particular, we describe the iterative team efforts that led us to gradually improve our codebase and eventually make Scikit-Learn's Random Forests one of the most efficient implementations in the scientific ecosystem, across all libraries and programming languages. Algorithmic and technical optimizations that have made this possible include:
- An efficient formulation of the decision tree algorithm, tailored for Random Forests;
- Cythonization of the tree induction algorithm;
- CPU cache optimizations, through low-level organization of data into contiguous memory blocks;
- Efficient multi-threading through GIL-free routines;
- A dedicated sorting procedure, taking into account the properties of data;
- Shared pre-computations whenever critical.
Overall, we believe that lessons learned from this case study extend to a broad range of scientific applications and may be of interest to anybody doing data analysis in Python.
Text Classification in Python – using Pandas, scikit-learn, IPython Notebook ...Jimmy Lai
Big data analysis relies on exploiting various handy tools to gain insight from data easily. In this talk, the speaker demonstrates a data mining flow for text classification using many Python tools. The flow consists of feature extraction/selection, model training/tuning and evaluation. Various tools are used in the flow, including: Pandas for feature processing, scikit-learn for classification, IPython, Notebook for fast sketching, matplotlib for visualization.
Gradient Boosted Regression Trees in scikit-learnDataRobot
Slides of the talk "Gradient Boosted Regression Trees in scikit-learn" by Peter Prettenhofer and Gilles Louppe held at PyData London 2014.
Abstract:
This talk describes Gradient Boosted Regression Trees (GBRT), a powerful statistical learning technique with applications in a variety of areas, ranging from web page ranking to environmental niche modeling. GBRT is a key ingredient of many winning solutions in data-mining competitions such as the Netflix Prize, the GE Flight Quest, or the Heritage Health Price.
I will give a brief introduction to the GBRT model and regression trees -- focusing on intuition rather than mathematical formulas. The majority of the talk will be dedicated to an in depth discussion how to apply GBRT in practice using scikit-learn. We will cover important topics such as regularization, model tuning and model interpretation that should significantly improve your score on Kaggle.
Effective Numerical Computation in NumPy and SciPyKimikazu Kato
Presented at PyCon JP 2014.
Video is available at
http://bit.ly/1tXYhw6
This talk explores case studies of effective usage of Numpy/Scipy and shows that the computational speed sometimes improves drastically with the appropriate derivation of formulas and performance-conscious implementation. I especially focus on scipy.sparse, the module for sparse matrices, which is often useful in the areas of machine learning and natural language processing.
Python training slides for beginners that I lecture. Starts from python versions to popular 3 party libraries. Arguments, interactive modes, variable types, language keywords, functions, class etc. If you want to get training please look www.ismailbaydan.com for more details.
Did you know that Python preallocates integers from -5 to 257? Reusing them 1000 times, instead of allocating memory for a bigger integer, can save you a couple milliseconds of code’s execution time. If you want to learn more about this kind of optimizations then, … well, probably this presentation is not for you :) Instead of going into such small details, I will talk about more “sane” ideas for writing faster code.
After a brief overview of how you can speed up your Python code in general, we will dig into source code optimization. I will show you some simple and fast ways of measuring the execution time of your code, and then we will discuss examples of how to improve some common code structures.
You will see:
* The fastest way of removing duplicates from a list
* How much faster your code is when you reuse the built-in functions instead of trying to reinvent the wheel
* What is faster than the “for loop”
* If the lookup is faster in a list or a set
* When it’s better to beg for forgiveness than to ask for permission
Pythran: Static compiler for high performance by Mehdi Amini PyData SV 2014PyData
Pythran is a an ahead of time compiler that turns modules written in a large subset of Python into C++ meta-programs that can be compiled into efficient native modules. It targets mainly compute intensive part of the code, hence it comes as no surprise that it focuses on scientific applications that makes extensive use of Numpy. Under the hood, Pythran inter-procedurally analyses the program and performs high level optimizations and parallel code generation. Parallelism can be found implicitly in Python intrinsics or Numpy operations, or explicitly specified by the programmer using OpenMP directives directly in the Python source code. Either way, the input code remains fully compatible with the Python interpreter. While the idea is similar to Parakeet or Numba, the approach differs significantly: the code generation is not performed at runtime but offline. Pythran generates C++11 heavily templated code that makes use of the NT2 meta-programming library and relies on any standard-compliant compiler to generate the binary code. We propose to walk through some examples and benchmarks, exposing the current state of what Pythran provides as well as the limit of the approach.
Introduction to Python 01-08-2023.pon by everyone else. . Hence, they must be...DRVaibhavmeshram1
Python
Language
is uesd in engineeringStory adapted from Stephen Covey (2004) “The Seven Habits of Highly Effective People” Simon & Schuster).
“Management is doing things right, leadership is doing the right things”
(Warren Bennis and Peter Drucker)
Story adapted from Stephen Covey (2004) “The Seven Habits of Highly Effective People” Simon & Schuster).
“Management is doing things right, leadership is doing the right things”
(Warren Bennis and Peter Drucker)
Story adapted from Stephen Covey (2004) “The Seven Habits of Highly Effective People” Simon & Schuster).
“Management is doing things right, leadership is doing the right things”
(Warren Bennis and Peter Drucker)
The Sponsor:
Champion and advocates for the change at their level in the organization.
A Sponsor is the person who won’t let the change initiative die from lack of attention, and is willing to use their political capital to make the change happen
The Role model:
Behaviors and attitudes demonstrated by them are looked upon by everyone else. . Hence, they must be willing to go first.
Employees watch leaders for consistency between words and actions to see if they should believe the change is really going to happen.
The decision maker:
Leaders usually control resources such as people, budgets, and equipment, and thus have the authority to make decisions (as per their span of control) that affect the initiative.
During change, leaders must leverage their decision-making authority and choose the options that will support the initiative.
The Decision-Maker is decisive and sets priorities that support change.
The Sponsor:
Champion and advocates for the change at their level in the organization.
A Sponsor is the person who won’t let the change initiative die from lack of attention, and is willing to use their political capital to make the change happen
The Role model:
Behaviors and attitudes demonstrated by them are looked upon by everyone else. . Hence, they must be willing to go first.
Employees watch leaders for consistency between words and actions to see if they should believe the change is really going to happen.
The decision maker:
Leaders usually control resources such as people, budgets, and equipment, and thus have the authority to make decisions (as per their span of control) that affect the initiative.
During change, leaders must leverage their decision-making authority and choose the options that will support the initiative.
The Decision-Maker is decisive and sets priorities that support change.
The Sponsor:
Champion and advocates for the change at their level in the organization.
A Sponsor is the person who won’t let the change initiative die from lack of attention, and is willing to use their political capital to make the change happen
The Role model:
Behaviors and attitudes demonstrated by them are looked upon by everyone else. . Hence, they must be willing to go first.
Employees watch leaders for consistency between words and actions to see if they s
Uma equipe de apenas 14 engenheiros (junho de 2017) cuida da Infraestrutura do principal banco de dados do Facebook. Toda ação no Instagram, Messenger, WhatsApp e claro, no FB, passa direta ou indiretamente pela infra de dezenas de milhares de servidores que rodam MySQL.
A linguagem usada pela equipe e por trás de toda a automação é Python. Nessa palestra, vamos mostrar como a linguagem possibilitou que chegássemos nessa escala, passando pela evolução, desafios e futuro:
Interfaces “tipadas” com Thrift;
Empacotamento através do Buck;
Type Checking com MyPy;
Asyncio para novos serviços;
Debugging com gdb 7 e pudb;
Além disso, durante a palestra, serão discutidas algumas decisões relacionadas a DevOps que podem inspirar soluções em outros ambientes: gerenciamento de dezenas de milhares de servidores e banco de dados, backups e restores contínuos, schema migrations, entre outros.
Python for R developers and data scientistsLambda Tree
This is an introductory talk aimed at data scientists who are well versed with R but would like to work with Python as well. I will cover common workflows in R and how they translate into Python. No Python experience necessary.
Chapter 22. Lambda Expressions and LINQIntro C# Book
In this chapter we will become acquainted with some of the advanced capabilities of the C# language. To be more specific, we will pay attention on how to make queries to collections, using lambda expressions and LINQ, and how to add functionality to already created classes, using extension methods. We will get to know the anonymous types, describe their usage briefly and discuss lambda expressions and show in practice how most of the built-in lambda functions work. Afterwards, we will pay more attention to the LINQ syntax – we will learn what it is, how it works and what queries we can build with it. In the end, we will get to know the meaning of the keywords in LINQ, and demonstrate their capabilities with lots of examples.
TROUBLESHOOTING 9 TYPES OF OUTOFMEMORYERRORTier1 app
Even though at surface level ‘java.lang.OutOfMemoryError’ appears as one single error; underlyingly there are 9 types of OutOfMemoryError. Each type of OutOfMemoryError has different causes, diagnosis approaches and solutions. This session equips you with the knowledge, tools, and techniques needed to troubleshoot and conquer OutOfMemoryError in all its forms, ensuring smoother, more efficient Java applications.
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
Why React Native as a Strategic Advantage for Startup Innovation.pdfayushiqss
Do you know that React Native is being increasingly adopted by startups as well as big companies in the mobile app development industry? Big names like Facebook, Instagram, and Pinterest have already integrated this robust open-source framework.
In fact, according to a report by Statista, the number of React Native developers has been steadily increasing over the years, reaching an estimated 1.9 million by the end of 2024. This means that the demand for this framework in the job market has been growing making it a valuable skill.
But what makes React Native so popular for mobile application development? It offers excellent cross-platform capabilities among other benefits. This way, with React Native, developers can write code once and run it on both iOS and Android devices thus saving time and resources leading to shorter development cycles hence faster time-to-market for your app.
Let’s take the example of a startup, which wanted to release their app on both iOS and Android at once. Through the use of React Native they managed to create an app and bring it into the market within a very short period. This helped them gain an advantage over their competitors because they had access to a large user base who were able to generate revenue quickly for them.
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
Modern design is crucial in today's digital environment, and this is especially true for SharePoint intranets. The design of these digital hubs is critical to user engagement and productivity enhancement. They are the cornerstone of internal collaboration and interaction within enterprises.
Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
For more details, visit us : https://informapuae.com/field-staff-tracking/
Advanced Flow Concepts Every Developer Should KnowPeter Caitens
Tim Combridge from Sensible Giraffe and Salesforce Ben presents some important tips that all developers should know when dealing with Flows in Salesforce.
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
Your Digital Assistant.
Making complex approach simple. Straightforward process saves time. No more waiting to connect with people that matter to you. Safety first is not a cliché - Securely protect information in cloud storage to prevent any third party from accessing data.
Would you rather make your visitors feel burdened by making them wait? Or choose VizMan for a stress-free experience? VizMan is an automated visitor management system that works for any industries not limited to factories, societies, government institutes, and warehouses. A new age contactless way of logging information of visitors, employees, packages, and vehicles. VizMan is a digital logbook so it deters unnecessary use of paper or space since there is no requirement of bundles of registers that is left to collect dust in a corner of a room. Visitor’s essential details, helps in scheduling meetings for visitors and employees, and assists in supervising the attendance of the employees. With VizMan, visitors don’t need to wait for hours in long queues. VizMan handles visitors with the value they deserve because we know time is important to you.
Feasible Features
One Subscription, Four Modules – Admin, Employee, Receptionist, and Gatekeeper ensures confidentiality and prevents data from being manipulated
User Friendly – can be easily used on Android, iOS, and Web Interface
Multiple Accessibility – Log in through any device from any place at any time
One app for all industries – a Visitor Management System that works for any organisation.
Stress-free Sign-up
Visitor is registered and checked-in by the Receptionist
Host gets a notification, where they opt to Approve the meeting
Host notifies the Receptionist of the end of the meeting
Visitor is checked-out by the Receptionist
Host enters notes and remarks of the meeting
Customizable Components
Scheduling Meetings – Host can invite visitors for meetings and also approve, reject and reschedule meetings
Single/Bulk invites – Invitations can be sent individually to a visitor or collectively to many visitors
VIP Visitors – Additional security of data for VIP visitors to avoid misuse of information
Courier Management – Keeps a check on deliveries like commodities being delivered in and out of establishments
Alerts & Notifications – Get notified on SMS, email, and application
Parking Management – Manage availability of parking space
Individual log-in – Every user has their own log-in id
Visitor/Meeting Analytics – Evaluate notes and remarks of the meeting stored in the system
Visitor Management System is a secure and user friendly database manager that records, filters, tracks the visitors to your organization.
"Secure Your Premises with VizMan (VMS) – Get It Now"
How Recreation Management Software Can Streamline Your Operations.pptxwottaspaceseo
Recreation management software streamlines operations by automating key tasks such as scheduling, registration, and payment processing, reducing manual workload and errors. It provides centralized management of facilities, classes, and events, ensuring efficient resource allocation and facility usage. The software offers user-friendly online portals for easy access to bookings and program information, enhancing customer experience. Real-time reporting and data analytics deliver insights into attendance and preferences, aiding in strategic decision-making. Additionally, effective communication tools keep participants and staff informed with timely updates. Overall, recreation management software enhances efficiency, improves service delivery, and boosts customer satisfaction.
Understanding Globus Data Transfers with NetSageGlobus
NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
3. How to install Python?
Download and use the Anaconda python distribution
https://store.continuum.io/cshop/anaconda/. It comes with
all the scientific python stack.
Alternatives: linux packages, pythonxy, canopy, . . .
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5. Basic types
Integer >>> 5
5
>>> a = 5
>>> a
5
Float >>> pi = 3.14
complex >>> c = 1 - 1j
boolean >>> b = 5 > 3 # 5 <= 3
>>> b
True # False
string >>> s = ’hello!’ # Also works with "hello!"
>>> s
’hello !’
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6. Python is a dynamic program language
Variable types are implicitly inferred during the assignment.
Variables are not declared.
>>> # In python
>>> a = 1
By contrast in statically typed language, you must declared the
type.
// In java, c, c++
int a = 1
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7. Numbers and their arithmetic operations (+,-,/,//,*,**,%)
>>> 1 + 2
4
>>> 50 - 5 * 6
20
>>> 2 / 3 # with py3 0.66...
0
>>> 2. / 3 # float division in py2 and py3
0.6666666666666666
>>> 4 // 3 # Integer division with py2 and py3
1
>>> 5 ** 3.5 # exponent
279.5084971874737
>>> 4 % 2 # modulo operation
0
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8. Playing with strings
>>> s = ’Great day!’
>>> s
’Great day!’
>>> s[0] # strings are sequences
’G’
>>> """A very
very long string
"""
’A verynvery long stringn’
>>> ’i={0} f={2} s={1}’.format(1, ’test’, 3.14)
’i=1 f=3.14 s=test’
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9. list, an ordered collection of objects
Instantiation >>> l = [] # an empty list
>>> l = [’spam’, ’egg’, [’another list’], 42]
Indexing >>> l[1]
’egg’
>>> l[-1] # n_elements - 1
42
>>> l[1:2] # a slice
["egg", [’another list’]]
Methods >>> len(l)
4
>>> l.pop(0)
’spam’
>>> l.append(3)
>>> l
[’egg’, [’another list’], 42, 3]
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10. dict, an unordered and associative data structure of
key-value pairs
Instantiation >>> d = {1: "a", "b": 2, 0: [4, 5, 6]}
>>> d
{0: [4, 5, 6], 1: ’a’, ’b’: 2}
Indexing >>> d[’b’]
2
>>> ’b’ in d
True
Insertion >>> d[’new’] = 56
>>> d
{0: [4, 5, 6], 1: ’a’, ’b’: 2, ’new’: 56}
Deletion >>> del d[’new’]
>>> d
{0: [4, 5, 6], 1: ’a’, ’b’: 2}
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11. dict, an unordered and associative data structure of
key-value pairs
Methods >>> len(d)
3
>>> d.keys()
[0, 1, ’b’]
>>> d.values()
[[4, 5, 6], ’a’, 2]
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12. Control flow: if / elif / else
>>> x = 3
>>> if x == 0:
... print("zero")
... elif x == 1:
... print("one")
... else:
... print("A big number")
...
’A big number’
Each indentation level corresponds to a block of code
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13. Control flow: for loop
>>> l = [0, 1, 2, 3]
>>> for a in l: # Iterate over a sequence
... print(a ** 2)
0
1
4
Iterating over sequence of numbers is easy with the range built-in.
>>> range(3)
[0, 1, 2]
>>> range(3, 10, 3)
[3, 6, 9]
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14. Control flow: while
>>> a, b = 0, 1
>>> while b < 50: # while True do ...
... a, b = b, a + b
... print(a)
...
1
1
2
3
5
8
13
21
34
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15. Control flow: functions
>>> def f(x, e=2):
... return x ** e
...
>>> f(3)
9
>>> f(5, 3)
125
>>> f(5, e=3)
125
Function arguments are passed by reference in python. Be aware of
side effects: mutable default parameters, inplace modifications of
the arguments.
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16. Classes and object
>>> class Counter:
... def __init__(self, initial_value=0):
... self.value = initial_value
... def inc(self):
... self.value += 1
...
>>> c = Counter() # Instantiate a counter object
>>> c.value # Access to an attribute
0
>>> c.inc() # Call a method
>>> c.value
1
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17. Import a package
>>> import math
>>> math.log(3)
1.0986122886681098
>>> from math import log
>>> log(4)
1.3862943611198906
You can try "import this" and "import antigravity".
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18. Python reference and tutorial
I Python Tutorial : http://docs.python.org/tutorial/
I Python Reference : https://docs.python.org/library/
How to use the "?" in ipython?
In [0]: d = {"a": 1}
In [1]: d?
Type: dict
String Form:{’a’: 1}
Length: 1
Docstring:
dict() -> new empty dictionary
dict(mapping) -> new dictionary initialized from a mapping object’s
(key, value) pairs
dict(iterable) -> new dictionary initialized as if via:
d = {}
for k, v in iterable:
d[k] = v
dict(**kwargs) -> new dictionary initialized with the name=value pairs
in the keyword argument list. For example: dict(one=1, two=2)
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20. NumPy
NumPy is the fundamental package for scientific computing with
Python. It contains among other things:
I a powerful N-dimensional array object,
I sophisticated (broadcasting) functions,
I tools for integrating C/C++ and Fortran code,
I useful linear algebra, Fourier transform, and random number
capabilities
With SciPy, it’s a replacement for MATLAB(c).
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21. 1-D numpy arrays
Let’s import the package.
>>> import numpy as np
Let’s create a 1-dimensional array.
>>> a = np.array([0, 1, 2, 3])
>>> a
array([0, 1, 2, 3])
>>> a.ndim
1
>>> a.shape
(4,)
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22. 2-D numpy arrays
Let’s import the package.
>>> import numpy as np
Let’s create a 2-dimensional array.
>>> b = np.array([[0, 1, 2], [3, 4, 5]])
>>> b
array([[ 0, 1, 2],
[ 3, 4, 5]])
>>> b.ndim
2
>>> b.shape
(2, 3)
Routine to create array: np.ones, np.zeros,. . .
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23. Array operations
>>> a = np.ones(3) / 5.
>>> b = np.array([1, 2, 3])
>>> a + b
array([ 1.2, 2.2, 3.2])
>>> np.dot(a, b)
1.200000
>>> ...
Many functions to operate efficiently on arrays : np.max, np.min,
np.mean, np.unique, . . .
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25. Reference and documentation
I NumPy User Guide:
http://docs.scipy.org/doc/numpy/user/
I NumPy Reference:
http://docs.scipy.org/doc/numpy/reference/
I MATLAB to NumPy:
http://wiki.scipy.org/NumPy_for_Matlab_Users
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27. scikit-learn Machine Learning in Python
I Simple and efficient tools for data mining and data analysis
I Accessible to everybody, and reusable in various contexts
I Built on NumPy, SciPy, and matplotlib
I Open source, commercially usable - BSD license
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28. A bug or need help?
I Mailing-list:
scikit-learn-general@lists.sourceforge.net;
I Tag scikit-learn on Stack Overflow.
How to install?
I It’s shipped with Anaconda.
I http://scikit-learn.org/stable/install.html
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29. Digits classification task
# Load some data
from sklearn.datasets import load_digits
digits = load_digits()
X, y = digits.data, digits.target
How can we build a system to classify images?
What is the first step?
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30. Data exploration and visualization
# Data visualization
import matplotlib.pyplot as plt
plt.gray()
plt.matshow(digits.images[0])
plt.show()
What else can be done?
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31. Fit a supervised learning model
from sklearn.svm import SVC
clf = SVC() # Instantiate a classifier
# API The base object, implements a fit method to learn from clf.fit(X, y) # Fit a classifier with the learning samples
# API Exploit the fitted model to make prediction
clf.predict(X)
# API Get a goodness of fit given data (X, y)
clf.score(X, y) # accuracy=1.
What do you think about this score of 1.?
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32. Cross validation
from sklearn.svm import SVC
from sklearn.cross_validation import KFold
scores = []
for train, test in KFold(len(X), n_folds=5, shuffle=True):
X_train, y_train = X[train], y[train]
X_test, y_test = X[test], y[test]
clf = SVC()
clf.fit(X_train, y_train)
scores.append(clf.score(X_test, y_test))
print(np.mean(scores)) # 0.44... !
What do you think about this score of 0.44?
Tip: This could be simplified using the cross_val_score function.
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33. Hyper-parameter optimization
from sklearn.svm import SVC
from sklearn.cross_validation import cross_val_score
parameters = np.linspace(0.0001, 0.01, num=10)
scores = []
for value in parameters:
clf = SVC(gamma=value)
s = cross_val_score(clf, X, y=y, cv=5)
scores.append(np.mean(s, axis=0))
print(np.max(scores)) # 0.97... !
Tip: This could be simplified using the GridSearchCV
meta-estimator.
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35. Estimator cooking: transformer union and pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
# API Transformer has a transform method
clf = make_pipeline(StandardScaler(),
# More transformers here
SVC())
from sklearn.pipeline import make_union
from sklearn.preprocessing import PolynomialFeatures
union_transformers = make_union(StandardScaler(),
# More transformers here
PolynomialFeatures())
clf = make_pipeline(union_transformers, SVC())
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36. Model persistence
from sklearn.externals import joblib
# Save the model for later
joblib.dump(clf, "model.joblib")
# Load the model
clf = joblib.load("model.joblib")
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37. Reference and documentation
I User Guide:
http://scikit-learn.org/stable/user_guide.html
I Reference: http:
//scikit-learn.org/stable/modules/classes.html
I Examples: http:
//scikit-learn.org/stable/auto_examples/index.html
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