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What are you focused on learning right now? From Rust and Python to Italian, here's what our community of maintainers are educating themselves on. Read more of their stories here: https://t.co/0fmuxQyYM2 https://t.co/VwfH6YUjSX Source: https://twitter.com/github/status/1555626135781875712 Uploader: GitHub

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The "Python Machine Learning (2nd edition)" book code repository and info resource Python Machine Learning (2nd Ed.) Code Repository Python Machine Learning, 2nd Ed. published September 20th, 2017 Paperback: 622 pages Publisher: Packt Publishing Language: English ISBN-10: 1787125939 ISBN-13: 978-1787125933 Kindle ASIN: B0742K7HYF Links Amazon Page Packt Page Table of Contents and Code Notebooks Helpful installation and setup instructions can be found in the README.md file of Chapter 1 To access the code materials for a given chapter, simply click on the open dir links next to the chapter headlines to navigate to the chapter subdirectories located in the code/ subdirectory. You can also click on the ipynb links below to open and view the Jupyter notebook of each chapter directly on GitHub. In addition, the code/ subdirectories also contain .py script files, which were created from the Jupyter Notebooks. However, I highly recommend working with the Jupyter notebook if possible in your computing environment. Not only do the Jupyter notebooks contain the images and section headings for easier navigation, but they also allow for a stepwise execution of individual code snippets, which -- in my opinion -- provide a better learning experience. Please note that these are just the code examples accompanying the book, which I uploaded for your convenience; be aware that these notebooks may not be useful without the formulae and descriptive text. Machine Learning - Giving Computers the Ability to Learn from Data [[open dir](./code/ch01)] [[ipynb](./code/ch01/ch01.ipynb)] Training Machine Learning Algorithms for Classification [[open dir](./code/ch02)] [[ipynb](./code/ch02/ch02.ipynb)] A Tour of Machine Learning Classifiers Using Scikit-Learn [[open dir](./code/ch03)] [[ipynb](./code/ch03/ch03.ipynb)] Building Good Training Sets – Data Pre-Processing [[open dir](./code/ch04)] [[ipynb](./code/ch04/ch04.ipynb)] Compressing Data via Dimensionality Reduction [[open dir](./code/ch05)] [[ipynb](./code/ch05/ch05.ipynb)] Learning Best Practices for Model Evaluation and Hyperparameter Optimization [[open dir](./code/ch06)] [[ipynb](./code/ch06/ch06.ipynb)] Combining Different Models for Ensemble Learning [[open dir](./code/ch07)] [[ipynb](./code/ch07/ch07.ipynb)] Applying Machine Learning to Sentiment Analysis [[open dir](./code/ch08)] [[ipynb](./code/ch08/ch08.ipynb)] Embedding a Machine Learning Model into a Web Application [[open dir](./code/ch09)] [[ipynb](./code/ch09/ch09.ipynb)] Predicting Continuous Target Variables with Regression Analysis [[open dir](./code/ch10)] [[ipynb](./code/ch10/ch10.ipynb)] Working with Unlabeled Data – Clustering Analysis [[open dir](./code/ch11)] [[ipynb](./code/ch11/ch11.ipynb)] Implementing a Multi-layer Artificial Neural Network from Scratch [[open dir](./code/ch12)] [[ipynb](./code/ch12/ch12.ipynb)] Parallelizing Neural Network Training with TensorFlow [[open dir](./code/ch13)] [[ipynb](./code/ch13/ch13.ipynb)] Going Deeper: The Mechanics of TensorFlow [[open dir](./code/ch14)] [[ipynb](./code/ch14/ch14.ipynb)] Classifying Images with Deep Convolutional Neural Networks [[open dir](./code/ch15)] [[ipynb](./code/ch15/ch15.ipynb)] Modeling Sequential Data Using Recurrent Neural Networks [[open dir](./code/ch16)] [[ipynb](./code/ch16/ch16.ipynb)] What’s new in the second edition from the first edition? Oh, there are so many things that we improved or added; where should I start!? The one issue on top of my priority list was to fix all the nasty typos that were introduced during the layout stage or my oversight. I really appreciated all the helpful feedback from readers in this manner! Furthermore, I addressed all the feedback about sections that may have been confusing or a bit unclear, reworded paragraphs, and added additional explanations. Also, special thanks go to the excellent editors of the second edition, who helped a lot along the way! Also, the figures and plots became much prettier. While readers liked the graphic content a lot, some people criticized the PowerPoint-esque style and layout. Thus, I decided to overhaul every little figure with a hopefully more pleasing choice of fonts and colors. Also, the data plots look much nicer now, thanks to the matplotlib team who put a lot of work in matplotlib 2.0 and its new styling theme. Beyond all these cosmetic fixes, new sections were added here and there. Among these is, for example, is a section on dealing with imbalanced datasets, which several readers were missing in the first edition and short section on Latent Dirichlet Allocation among others. As time and the software world moved on after the first edition was released in September 2015, we decided to replace the introduction to deep learning via Theano. No worries, we didn't remove it but it got a substantial overhaul and is now based on TensorFlow, which has become a major player in my research toolbox since its open source release by Google in November 2015. Along with the new introduction to deep learning using TensorFlow, the biggest additions to this new edition are three brand new chapters focussing on deep learning applications: A more detailed overview of the TensorFlow mechanics, an introduction to convolutional neural networks for image classification, and an introduction to recurrent neural networks for natural language processing. Of course, and in a similar vein as the rest of the book, these new chapters do not only provide readers with practical instructions and examples but also introduce the fundamental mathematics behind those concepts, which are an essential building block for understanding how deep learning works. [ [Excerpt from "Machine Learning can be useful in almost every problem domain:" An interview with Sebastian Raschka](https://www.packtpub.com/books/content/machine-learning-useful-every-problem-domain-interview-sebastian-raschka/) ] Raschka, Sebastian, and Vahid Mirjalili. Python Machine Learning, 2nd Ed . Packt Publishing, 2017. @book{RaschkaMirjalili2017, address = {Birmingham, UK}, author = {Raschka, Sebastian and Mirjalili, Vahid}, edition = {2}, isbn = {978-1787125933}, keywords = {Clustering,Data Science,Deep Learning, Machine Learning,Neural Networks,Programming, Supervised Learning}, publisher = {Packt Publishing}, title = {{Python Machine Learning, 2nd Ed.}}, year = {2017} } Translations German ISBN-10: 3958457339 ISBN-13: 978-3958457331 Amazon.de link Publisher link Japanese ISBN-10: 4295003379 ISBN-13: 978-4295003373 Amazon.co.jp link To restore the repository download the bundle wget https://archive.org/download/github.com-rasbt-python-machine-learning-book-2nd-edition_-_2019-11-15_20-49-49/rasbt-python-machine-learning-book-2nd-edition_-_2019-11-15_20-49-49.bundle and run: git clone rasbt-python-machine-learning-book-2nd-edition_-_2019-11-15_20-49-49.bundle Source: https://github.com/rasbt/python-machine-learning-book-2nd-edition Uploader: rasbt Upload date: 2019-11-15

This article is from Frontiers in Neuroinformatics , volume 8 . Abstract The aim in high-resolution connectomics is to reconstruct complete neuronal connectivity in a tissue. Currently, the only technology capable of resolving the smallest neuronal processes is electron microscopy (EM). Thus, a common approach to network reconstruction is to perform (error-prone) automatic segmentation of EM images, followed by manual proofreading by experts to fix errors. We have developed an algorithm and software library to not only improve the accuracy of the initial automatic segmentation, but also point out the image coordinates where it is likely to have made errors. Our software, called gala (graph-based active learning of agglomeration), improves the state of the art in agglomerative image segmentation. It is implemented in Python and makes extensive use of the scientific Python stack (numpy, scipy, networkx, scikit-learn, scikit-image, and others). We present here the software architecture of the gala library, and discuss several designs that we consider would be generally useful for other segmentation packages. We also discuss the current limitations of the gala library and how we intend to address them.

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After Work Data Science Ensemble Learning With Python Project

Python competency is one of the most in-demand skills in the technical realm. Being a mainstay language, Python support many futuristic technologies such as Data Science, Machine Learning, Cloud Computing, Artificial Intelligence, and Data Visualization, learning Python has become indispensable. If you also want to dip your toes in Python programming , this blog has the right information for you. If you are in search of the Best Python Bootcamps in US, SynergisticIT is the first choice you can make.

Learning Python, 4th Edition

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.

48 pages ; 28 cm

The "Python Machine Learning (2nd edition)" book code repository and info resource Python Machine Learning (2nd Ed.) Code Repository Python Machine Learning, 2nd Ed. published September 20th, 2017 Paperback: 622 pages Publisher: Packt Publishing Language: English ISBN-10: 1787125939 ISBN-13: 978-1787125933 Kindle ASIN: B0742K7HYF Links Amazon Page Packt Page Table of Contents and Code Notebooks Helpful installation and setup instructions can be found in the README.md file of Chapter 1 To access the code materials for a given chapter, simply click on the open dir links next to the chapter headlines to navigate to the chapter subdirectories located in the code/ subdirectory. You can also click on the ipynb links below to open and view the Jupyter notebook of each chapter directly on GitHub. In addition, the code/ subdirectories also contain .py script files, which were created from the Jupyter Notebooks. However, I highly recommend working with the Jupyter notebook if possible in your computing environment. Not only do the Jupyter notebooks contain the images and section headings for easier navigation, but they also allow for a stepwise execution of individual code snippets, which -- in my opinion -- provide a better learning experience. Please note that these are just the code examples accompanying the book, which I uploaded for your convenience; be aware that these notebooks may not be useful without the formulae and descriptive text. Machine Learning - Giving Computers the Ability to Learn from Data [[open dir](./code/ch01)] [[ipynb](./code/ch01/ch01.ipynb)] Training Machine Learning Algorithms for Classification [[open dir](./code/ch02)] [[ipynb](./code/ch02/ch02.ipynb)] A Tour of Machine Learning Classifiers Using Scikit-Learn [[open dir](./code/ch03)] [[ipynb](./code/ch03/ch03.ipynb)] Building Good Training Sets – Data Pre-Processing [[open dir](./code/ch04)] [[ipynb](./code/ch04/ch04.ipynb)] Compressing Data via Dimensionality Reduction [[open dir](./code/ch05)] [[ipynb](./code/ch05/ch05.ipynb)] Learning Best Practices for Model Evaluation and Hyperparameter Optimization [[open dir](./code/ch06)] [[ipynb](./code/ch06/ch06.ipynb)] Combining Different Models for Ensemble Learning [[open dir](./code/ch07)] [[ipynb](./code/ch07/ch07.ipynb)] Applying Machine Learning to Sentiment Analysis [[open dir](./code/ch08)] [[ipynb](./code/ch08/ch08.ipynb)] Embedding a Machine Learning Model into a Web Application [[open dir](./code/ch09)] [[ipynb](./code/ch09/ch09.ipynb)] Predicting Continuous Target Variables with Regression Analysis [[open dir](./code/ch10)] [[ipynb](./code/ch10/ch10.ipynb)] Working with Unlabeled Data – Clustering Analysis [[open dir](./code/ch11)] [[ipynb](./code/ch11/ch11.ipynb)] Implementing a Multi-layer Artificial Neural Network from Scratch [[open dir](./code/ch12)] [[ipynb](./code/ch12/ch12.ipynb)] Parallelizing Neural Network Training with TensorFlow [[open dir](./code/ch13)] [[ipynb](./code/ch13/ch13.ipynb)] Going Deeper: The Mechanics of TensorFlow [[open dir](./code/ch14)] [[ipynb](./code/ch14/ch14.ipynb)] Classifying Images with Deep Convolutional Neural Networks [[open dir](./code/ch15)] [[ipynb](./code/ch15/ch15.ipynb)] Modeling Sequential Data Using Recurrent Neural Networks [[open dir](./code/ch16)] [[ipynb](./code/ch16/ch16.ipynb)] What’s new in the second edition from the first edition? Oh, there are so many things that we improved or added; where should I start!? The one issue on top of my priority list was to fix all the nasty typos that were introduced during the layout stage or my oversight. I really appreciated all the helpful feedback from readers in this manner! Furthermore, I addressed all the feedback about sections that may have been confusing or a bit unclear, reworded paragraphs, and added additional explanations. Also, special thanks go to the excellent editors of the second edition, who helped a lot along the way! Also, the figures and plots became much prettier. While readers liked the graphic content a lot, some people criticized the PowerPoint-esque style and layout. Thus, I decided to overhaul every little figure with a hopefully more pleasing choice of fonts and colors. Also, the data plots look much nicer now, thanks to the matplotlib team who put a lot of work in matplotlib 2.0 and its new styling theme. Beyond all these cosmetic fixes, new sections were added here and there. Among these is, for example, is a section on dealing with imbalanced datasets, which several readers were missing in the first edition and short section on Latent Dirichlet Allocation among others. As time and the software world moved on after the first edition was released in September 2015, we decided to replace the introduction to deep learning via Theano. No worries, we didn't remove it but it got a substantial overhaul and is now based on TensorFlow, which has become a major player in my research toolbox since its open source release by Google in November 2015. Along with the new introduction to deep learning using TensorFlow, the biggest additions to this new edition are three brand new chapters focussing on deep learning applications: A more detailed overview of the TensorFlow mechanics, an introduction to convolutional neural networks for image classification, and an introduction to recurrent neural networks for natural language processing. Of course, and in a similar vein as the rest of the book, these new chapters do not only provide readers with practical instructions and examples but also introduce the fundamental mathematics behind those concepts, which are an essential building block for understanding how deep learning works. [ [Excerpt from "Machine Learning can be useful in almost every problem domain:" An interview with Sebastian Raschka](https://www.packtpub.com/books/content/machine-learning-useful-every-problem-domain-interview-sebastian-raschka/) ] Raschka, Sebastian, and Vahid Mirjalili. Python Machine Learning, 2nd Ed . Packt Publishing, 2017. @book{RaschkaMirjalili2017, address = {Birmingham, UK}, author = {Raschka, Sebastian and Mirjalili, Vahid}, edition = {2}, isbn = {978-1787125933}, keywords = {Clustering,Data Science,Deep Learning, Machine Learning,Neural Networks,Programming, Supervised Learning}, publisher = {Packt Publishing}, title = {{Python Machine Learning, 2nd Ed.}}, year = {2017} } Translations German ISBN-10: 3958457339 ISBN-13: 978-3958457331 Amazon.de link Publisher link Japanese ISBN-10: 4295003379 ISBN-13: 978-4295003373 Amazon.co.jp link To restore the repository download the bundle wget https://archive.org/download/github.com-rasbt-python-machine-learning-book-2nd-edition_-_2019-03-23_17-47-51/rasbt-python-machine-learning-book-2nd-edition_-_2019-03-23_17-47-51.bundle and run: git clone rasbt-python-machine-learning-book-2nd-edition_-_2019-03-23_17-47-51.bundle Source: https://github.com/rasbt/python-machine-learning-book-2nd-edition Uploader: rasbt Upload date: 2019-03-23

https://2019.za.pycon.org/talks/31-machine-learning-engineering-principles-with-python-and-mlflow/ Machine Learning is a very hyped topic of the moment. While a lot of the talks and presentations cover the data science component, very few cover the nity gritty details of a machine learning pipeline. This talk will focus on the engineering part of Machine Learning by covering different Machine Learning systems architecture best practices, strategies including design. We will delve into the essence of Uber's Michelangelo, Airbnbs s Bighead and Facebooks FB Learner. During the talk, I will use MLFlow and Python as platforms to create an open-source based solution similar to the ones from the big tech companies for the everyday tech startup. The entirety of the cycle of training, deployment, monitoring, champion/challenger testing, and serving layer will be addressed. Technical debt prevention is another topic that will be addressed in the end of the talk. Room: Ballroom

https://2019.za.pycon.org/talks/31-machine-learning-engineering-principles-with-python-and-mlflow/ Machine Learning is a very hyped topic of the moment. While a lot of the talks and presentations cover the data science component, very few cover the nity gritty details of a machine learning pipeline. This talk will focus on the engineering part of Machine Learning by covering different Machine Learning systems architecture best practices, strategies including design. We will delve into the essence of Uber's Michelangelo, Airbnbs s Bighead and Facebooks FB Learner. During the talk, I will use MLFlow and Python as platforms to create an open-source based solution similar to the ones from the big tech companies for the everyday tech startup. The entirety of the cycle of training, deployment, monitoring, champion/challenger testing, and serving layer will be addressed. Technical debt prevention is another topic that will be addressed in the end of the talk. Room: Ballroom

https://2019.za.pycon.org/talks/31-machine-learning-engineering-principles-with-python-and-mlflow/ Machine Learning is a very hyped topic of the moment. While a lot of the talks and presentations cover the data science component, very few cover the nity gritty details of a machine learning pipeline. This talk will focus on the engineering part of Machine Learning by covering different Machine Learning systems architecture best practices, strategies including design. We will delve into the essence of Uber's Michelangelo, Airbnbs s Bighead and Facebooks FB Learner. During the talk, I will use MLFlow and Python as platforms to create an open-source based solution similar to the ones from the big tech companies for the everyday tech startup. The entirety of the cycle of training, deployment, monitoring, champion/challenger testing, and serving layer will be addressed. Technical debt prevention is another topic that will be addressed in the end of the talk. Room: Ballroom

Programming has become a fundamental skill in the 21st century. Its uses are not only limited to computer science but a wide array of fields. We believe that the 7th-grade kids are mentally capable to learn python online , begin their programming journey, develop interest, be comfortable in handling large chunks of data and also solve complex problems.

Programming has become a fundamental skill in the 21st century. Its uses are not only limited to computer science but a wide array of fields. We believe that the 7th-grade kids are mentally capable to learn python online , begin their programming journey, develop interest, be comfortable in handling large chunks of data and also solve complex problems.

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As the scale and scope of data collection continue to increase across virtually all fields, statistical learning has become a critical toolkit for anyone who wishes to understand data. An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. This book is appropriate for anyone who wishes to use contemporary tools for data analysis.

Train passenger forecasting assists in planning, resource use, and system management. forecasts rail ridership. Train passenger predictions help prevent stranded passengers and empty seats. Simulating rail transport requires a low-error model. We developed a fuzzy time series forecasting model. Using historical data was the goal. This concept predicts future railway passengers using Holt's double exponential smoothing (DES) and a fuzzy time series technique based on a rate-of-change algorithm. Holt's DES predicts the next period using a fuzzy time series and the rate of change. This method improves prediction accuracy by using event discretization. positive, since changing dynamics reveal trends and seasonality. It uses event discretization and machine-learning-optimized frequency partitioning. The suggested method is compared to existing train passenger forecasting methods. This study has a low average forecasting error and a mean squared error.

Python is the lingua franca for data analytics and machine learning. Its superior productivity makes it the preferred tool for prototyping. However, traditional Python packages are not necessarily designed to provide high performance and scalability for large datasets. From this talk you will learn how to get close-to-native performance with Intel-optimized packages, such as numpy, scipy, and scikit-learn. The next part of the talk is focused on getting high performance and scalability from multi-cores on a single machine to large clusters of workstations. It will be demonstrated that with Python it is possible to achieve the same performance and scalability as with hand-tuned C++/MPI code: - Scalable Dataframe Compiler (SDC) makes possible to efficiently load and process huge datasets using pandas/Python. - A convenient Python API to data analytics and machine learning primitives (daal4py). While its interface is scikit-learn-like, its MPI-based engine allows to scale machine learning algorithms to bare-metal cluster performance. - From the talk you will learn how to use SDC and daal4py together to build an end-to-end analytics pipeline that scales to clusters, requiring only minimal code changes. Please see our speaker release agreement for details: https://ep2020.europython.eu/events/speaker-release-agreement/

These are solar wind in situ data arrays in python pickle format suitable for machine learning, i.e. the arrays consist only of numbers, no strings and no datetime objects. See AAREADME_insitu_ML.txt for more explanation. If you use these data for peer reviewed scientific publications, please get in touch concerning usage and possible co-authorship by the authors (C. Möstl, A. J. Weiss, R. L. Bailey, A. Isavnin, D. Stansby): [email protected] or twitter @chrisoutofspace Made with https://github.com/cmoestl/heliocats Load in python with e.g. for Parker Solar Probe data: > import pickle > filepsp='psp_2018_2019_sceq_ndarray.p' > [psp,hpsp]=pickle.load(open(filepsp, "rb" ) ) plot time vs total field > import matplotlib.pyplot as plt > plt.plot(psp['time'],psp['bt']) Times psp[:,0 ] or psp['time'] are in matplotlib format. Variable 'hpsp' contains a header with the variable names and units for each column. Coordinate systems for magnetic field components are RTN (Ulysses), SCEQ (Parker Solar Probe, STEREO-A/B, VEX, MESSENGER), HEEQ (Wind) available parameters: bt = total magnetic field bxyz = magnetic field components vt = total proton speed vxyz = velocity components (only for PSP) np = proton density tp = proton temperature xyz = spacecraft position in HEEQ r, lat, lon = spherical coordinates of position in HEEQ

Mastering Git Switch, Python Learning Sites, and Debugging Ruby on Rails" is a comprehensive guide for developers aiming to enhance their skills in essential areas of modern software development. This blog covers three critical topics: mastering the git switch command in Git for efficient branch management, exploring the best Python learning sites for programmers, and delving into advanced debugging techniques for Ruby on Rails applications. Know more - https://stackify.com/

Machine Learning With Python- Logistic Regression. This article demonstrates how to utilize the logistic regression classification algorithm in Python.

https://2019.za.pycon.org/talks/80-getting-machine-learning-models-ready-for-production-using-python/ As a Scientist, it’s incredibly satisfying to be given the freedom to experiment by applying new research and rapidly prototyping. This satisfaction can be sustained quite well in a lab environment but can diminish quickly in a corporate environment. This is because of the underlying commercial value motive which science is driven by in a business setting — if it doesn’t add business value to employees or customers, there’s no place for it! Business value, however, goes beyond just being a nifty experiment which shows potential value to employees or customers. In the context of Machine Learning models, the only [business] valuable models, are models in Production! In this talk, I will take the audience through the steps involved in moving from experiments in Jupyter Notebooks to automated model training, serving and deployments for Production using an array of Python tools such as Numpy, Pandas, SciKit Learn and Docker. The intended audience for this talk includes Data Scientists, Software Engineers and any other Data practitioners who have or want to go through the journey of gaining real-time value from Machine Learning models in Production. This talk will impart lessons learnt in moving from Jupyter experiments to writing production-ready Python code, as well as impart important Python tools, frameworks and libraries which can be used to accelerate such a transition. Room: Ballroom

130 pages : 23 cm

Perma.cc archive of https://www.analyticsvidhya.com/blog/2018/09/deep-learning-video-classification-python/? created on 2022-09-23 02:38:18.162776+00:00.

#มองโลกมองไทย ประจำวันที่ 25 ตุลาคม 2563 คุยกับคอมพิวเตอร์ด้วยภาษามนุษย์? ปราบ เลาหะโรจนพันธ์ อาจารย์พิเศษ วิชา Machine Learning ม.ธรรมศาสตร์ แขกรับเชิญพิเศษจะมาเล่าให้ฟังว่า 'ทักษะภาษาคอมพ์ฯ' ช่วยให้เราพลิกวิธีคิด พลิกอนาคตอย่างไร และทำไม โลกถึงนิยม และมองหาคนที่มีทักษะนี้เสียเหลือเกิน? Machine Learning สำหรับมนุษย์เงินเดือน โดย ThinkLab Creative Space & Cafe https://www.facebook.com/thinklab.creativespace/posts/194751758721175 พิเศษ! เพียงระบุว่ารับชมมาจากรายการ "มองโลก มองไทย" รับส่วนลด 10% Facebook Page: ThinkLab Creative Space and Cafe โทรศัพท์/ LINE ID: 0610247199 สมัครสมาชิกเพื่อรับสิทธิพิเศษ (Membership) https://www.youtube.com/channel/UCpHTAE2EOwWkWGnW2HY8gRw/join ติดตาม #VoiceTV YouTube : https://www.youtube.com/channel/UCpHTAE2EOwWkWGnW2HY8gRw Facebook : https://www.facebook.com/pg/VoiceTVRankingThailand Instagram : https://www.instagram.com/voicetv/ Twitter : https://twitter.com/VoiceTVOfficial Website : https://www.voicetv.co.th/ Source: https://www.youtube.com/watch?v=fUrtterb4Z0 Uploader: VOICE TV

Imbalanced-learn is an open-source python toolbox aiming at providing a wide range of methods to cope with the problem of imbalanced dataset frequently encountered in machine learning and pattern recognition. The implemented state-of-the-art methods can be categorized into 4 groups: (i) under-sampling, (ii) over-sampling, (iii) combination of over- and under-sampling, and (iv) ensemble learning methods. The proposed toolbox only depends on numpy, scipy, and scikit-learn and is distributed under MIT license. Furthermore, it is fully compatible with scikit-learn and is part of the scikit-learn-contrib supported project. Documentation, unit tests as well as integration tests are provided to ease usage and contribution. The toolbox is publicly available in GitHub: https://github.com/scikit-learn-contrib/imbalanced-learn.

Humans communicate with one another using body language (gestures), such as hand and head gestures, facial expressions, lip movements, and so forth, or through natural language channels like words and writing. Sign language comprehension is just as crucial as knowing normal language. The primary means of communication for those who are hard of hearing is sign language. Without a translation, speaking with other hearing people can be difficult for those with hearing impairments. Because of this, the social lives of deaf people would be greatly improved by the installation of a system that recognizes sign language. In order to recognize the features of the hand in pictures captured by a webcam, we have presented in this study a marker-free, visual American Sign Language recognition system that makes use of image processing, computer vision, and neural network techniques. This paper deals with full phrase gestures that are used regularly every day and methods used to converted them to text. A number of image processing techniques have been used to identify the hand shape from continuous pictures. The Haar Cascade Classifier is used to determine the interpretation of signs and their associated meaning.

Python Machine Learning Third Edition Machine Learning and Deep Learning with Python,  scikit-learn, and TensorFlow 2 Sebastian Raschka Vahid Mirjalili

To enhance developer productivity, all modern integrated development environments (IDEs) include code suggestion functionality that proposes likely next tokens at the cursor. While current IDEs work well for statically-typed languages, their reliance on type annotations means that they do not provide the same level of support for dynamic programming languages as for statically-typed languages. Moreover, suggestion engines in modern IDEs do not propose expressions or multi-statement idiomatic code. Recent work has shown that language models can improve code suggestion systems by learning from software repositories. This paper introduces a neural language model with a sparse pointer network aimed at capturing very long-range dependencies. We release a large-scale code suggestion corpus of 41M lines of Python code crawled from GitHub. On this corpus, we found standard neural language models to perform well at suggesting local phenomena, but struggle to refer to identifiers that are introduced many tokens in the past. By augmenting a neural language model with a pointer network specialized in referring to predefined classes of identifiers, we obtain a much lower perplexity and a 5 percentage points increase in accuracy for code suggestion compared to an LSTM baseline. In fact, this increase in code suggestion accuracy is due to a 13 times more accurate prediction of identifiers. Furthermore, a qualitative analysis shows this model indeed captures interesting long-range dependencies, like referring to a class member defined over 60 tokens in the past.

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Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (18 - Support Vector Machine (SVM))

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (44 - XGBoost)

Executing machine learning models in a production environment can be tricky, especially at a major bank where compliance and risk are carefully taken into account. In this talk I explain how, we, at ING (a large bank operating on global scale), execute our Python models in a production environment by building minimal Docker images for python versions. I will first talk about the possible security risks of running any docker container in a production environment. Then I will talk about ways in which we can make Docker containers more secure by building minimal docker images for Python. Finally I will explain how these docker images are used in practice to serve machine learning models at ING. Prerequisites: - Some basic knowledge of Docker can be helpful - Some basic understanding of security can be helpful Goals: - Understand the security risks of running docker containers - Know how to make docker images more secure - How to build secure model serving docker images Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/

random2, 'Andrew Park - Data Science for Beginners_ 4 Books in 1_ Python Programming, Data Analysis, Machine Learning. A Complete Overview to Master The Art of Data Science From Scratch Using Python for Busines'

Supervised Machine Learning with Python. Classification: Support Vector Machines

Shrey Somaiya https://2020.pycon.org.au/program/73HFNJ This talk is simply one about my experience of learning C from a python background - what python features helped me understand C better (control flow, algorithm and problem breakdown design, functions, etc) - what C features helped me understand python better (ie pointers in C helped me understand python lists, etc) - how I use my python knowledge to teach C at University python, pycon, australia, programming, conference, technical, pyconline, developers, panel, sessions, libraries, frameworks, community, sysadmins, students, education, data, science PyCon AU is the national conference for the Python programming community, bringing together professional, student and enthusiast developers, sysadmins and operations folk, students, educators, scientists, statisticians, and many others besides, all with a love for working with Python. PyCon AU informs the country's Python developers with presentations, tutorials and panel sessions by experts and core developers of Python, as well as the libraries and frameworks that they rely on. PyCon AU is the national conference for the Python programming community, bringing together professional, student and enthusiast developers, sysadmins and operations folk, students, educators, scientists, statisticians, and many others besides, all with a love for working with Python. PyCon AU informs the country's Python developers with presentations, tutorials and panel sessions by experts and core developers of Python, as well as the libraries and frameworks that they rely on. Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1 Python, PyCon, PyConAU, PyConline Fri Sep 4 10:25:00 2020 at Floperator

Learn Python from start to end

Welcome to the world of network programming with Python. Python is a full-featured object-oriented programming language with a standard library that includes everything needed to rapidly build powerful network applications. In addition, it has a multitude of third-party libraries and packages that extend Python to every sphere of network programming. Combined with the fun of using Python, with this book, we hope to get you started on your journey so that you master these tools and produce some great networking code. In this book, we are squarely targeting Python 3. Although Python 3 is still establishing itself as the successor to Python 2, version 3 is the future of the language, and we want to demonstrate that it is ready for network programming prime time. It offers many improvements over the previous version, many of which improve the network programming experience, with enhanced standard library modules and new additions. We hope you enjoy this introduction to network programming with Python. Dr. M. O. Faruque Sarker    Sam Washington

How To Think Like A Computer Scientist Learning Python

Learning Professional Python 1

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