Road to Winning at Horse Racing with Data ScienceShun Nukui
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This document discusses developing an AI system for predicting horse race outcomes in order to make a profit. It summarizes the project's goals of defining an objective function for predictions, feature engineering using over 1,500 horse racing metrics, and training a LightGBM model on the data. Evaluation is done using nDCG to measure prediction accuracy against different scoring systems like horse placement, odds, and popularity. The goal is to predict horses that the public may miss in order to have higher returns.
Text Classification in Python – using Pandas, scikit-learn, IPython Notebook ...Jimmy Lai
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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.
Chapitre4 Les sondages à probabilité inégalesMahamadou Haro
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Ce diaporama présente les sondages à probabilité inégales
The document describes a language detection library that can detect the language of texts with over 99% precision for 49 languages. It uses a Naive Bayes algorithm and character n-grams as features to classify texts into language categories. The library is open source and available for Java. It was tested on over 9,000 news articles in 49 languages with an accuracy of 99.77%.
TensorFlow Tutorial | Deep Learning With TensorFlow | TensorFlow Tutorial For...Simplilearn
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The document provides an overview of using TensorFlow to build deep learning models. It discusses how TensorFlow uses computational graphs to process data and perform computations. Tensors represent multi-dimensional data and are core to TensorFlow's operations. The document also demonstrates how to build simple models like linear regression and recurrent neural networks (RNNs) using TensorFlow. An example RNN model predicts monthly milk production using time series data.
Le Luxe connait une e-transformation comme tous les secteurs. Focus sur la distribution avec le développement du cross-canal et le développement services web qui ajoutent à l'expérience client luxe.
Sage ERP X3 Technology & Architecture ReviewNet at Work
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With the release of version 7 of Sage ERP X3 introduces an entirely new user experience with fully browser base system. This session review the new technology of Sage ERP X3 7.0.
netatwork.com/services_1/bizsol_1/sage-erp-accounting-software-solutions/sage-erp-x3/
Top contenders in the 2015 KDD cup include the team from DataRobot comprising Owen Zhang, #1 Ranked Kaggler and top Kagglers Xavier Contort and Sergey Yurgenson. Get an in-depth look as Xavier describes their approach. DataRobot allowed the team to focus on feature engineering by automating model training, hyperparameter tuning, and model blending - thus giving the team a firm advantage.
This document summarizes a presentation by Dr. Christoph Angerer on RAPIDS, an open source library for GPU-accelerated data science. Some key points:
- RAPIDS provides an end-to-end GPU-accelerated workflow for data science using CUDA and popular tools like Pandas, Spark, and XGBoost.
- It addresses challenges with data movement and formats by keeping data on the GPU as much as possible using the Apache Arrow data format.
- Benchmarks show RAPIDS provides significant speedups over CPU for tasks like data preparation, machine learning training, and visualization.
- Future work includes improving cuDF (GPU DataFrame library), adding algorithms to cuML
OmniCanal Luxe et Prêt-à-Porter : 9 Stratégies GagnantesStephany Gochuico
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SOMMAIRE OMNICANAL
- Retail OmniCanal, c’est quoi ?
- Comportement des consommateurs OmniCanal
- 9 Stratégies Gagnantes pour l’OmniCanal
- Meilleures Enseignes "OmniCanal"
- Les plus Grandes Marques de Luxe dans le monde
- Les marques de Luxe, devraient-elles devenir "OmniCanal" ?
- Comportement des Consommateurs Fortunés
- Comparatif comportemental des différentes générations
- Comment réussir l’OmniCanal ?
End-to-End Deep Learning Deployment with ONNXNick Pentreath
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The Open Neural Network Exchange (ONNX) standard has emerged for representing deep learning models in a standardized format. In this talk, I will discuss:
1. ONNX for exporting deep learning computation graphs, the ONNX-ML component of the specification for exporting both traditional ML models, common feature extraction, data transformation and post-processing steps.
2. How to use ONNX and the growing ecosystem of exporter libraries for common frameworks (including TensorFlow, PyTorch, Keras, scikit-learn and Apache SparkML) to deploy complete deep learning pipelines.
3. Best practices for working with and combining these disparate exporter toolkits, as well as highlight the gaps, issues, and missing pieces to be taken into account and still to be addressed.
Introduction to natural language processing (NLP)Alia Hamwi
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The document provides an introduction to natural language processing (NLP). It defines NLP as a field of artificial intelligence devoted to creating computers that can use natural language as input and output. Some key NLP applications mentioned include data analysis of user-generated content, conversational agents, translation, classification, information retrieval, and summarization. The document also discusses various linguistic levels of analysis like phonology, morphology, syntax, and semantics that involve ambiguity challenges. Common NLP tasks like part-of-speech tagging, named entity recognition, parsing, and information extraction are described. Finally, the document outlines the typical steps in an NLP pipeline including data collection, text cleaning, preprocessing, feature engineering, modeling and evaluation.
This document discusses obfuscation and mutations in malware. It begins by defining obfuscation as deliberately creating code that is difficult for humans to understand. It then describes various obfuscation techniques used in malware including dead code insertion, register reassignment, and subroutine reordering. The document also discusses different types of malware like viruses, worms, and trojans. It classifies malware into first and second generation and describes techniques used in second generation malware like encryption, oligomorphism, polymorphism, and metamorphism. The document concludes by explaining various malware detection methods such as signature-based, behavior-based, heuristic, and hybrid approaches.
The document describes a language detection library that can detect the language of texts with over 99% precision for 49 languages. It uses a Naive Bayes algorithm and character n-grams as features to classify texts into language categories. The library is open source and available for Java. It was tested on over 9,000 news articles in 49 languages with an accuracy of 99.77%.
TensorFlow Tutorial | Deep Learning With TensorFlow | TensorFlow Tutorial For...Simplilearn
?
The document provides an overview of using TensorFlow to build deep learning models. It discusses how TensorFlow uses computational graphs to process data and perform computations. Tensors represent multi-dimensional data and are core to TensorFlow's operations. The document also demonstrates how to build simple models like linear regression and recurrent neural networks (RNNs) using TensorFlow. An example RNN model predicts monthly milk production using time series data.
Le Luxe connait une e-transformation comme tous les secteurs. Focus sur la distribution avec le développement du cross-canal et le développement services web qui ajoutent à l'expérience client luxe.
Sage ERP X3 Technology & Architecture ReviewNet at Work
?
With the release of version 7 of Sage ERP X3 introduces an entirely new user experience with fully browser base system. This session review the new technology of Sage ERP X3 7.0.
netatwork.com/services_1/bizsol_1/sage-erp-accounting-software-solutions/sage-erp-x3/
Top contenders in the 2015 KDD cup include the team from DataRobot comprising Owen Zhang, #1 Ranked Kaggler and top Kagglers Xavier Contort and Sergey Yurgenson. Get an in-depth look as Xavier describes their approach. DataRobot allowed the team to focus on feature engineering by automating model training, hyperparameter tuning, and model blending - thus giving the team a firm advantage.
This document summarizes a presentation by Dr. Christoph Angerer on RAPIDS, an open source library for GPU-accelerated data science. Some key points:
- RAPIDS provides an end-to-end GPU-accelerated workflow for data science using CUDA and popular tools like Pandas, Spark, and XGBoost.
- It addresses challenges with data movement and formats by keeping data on the GPU as much as possible using the Apache Arrow data format.
- Benchmarks show RAPIDS provides significant speedups over CPU for tasks like data preparation, machine learning training, and visualization.
- Future work includes improving cuDF (GPU DataFrame library), adding algorithms to cuML
OmniCanal Luxe et Prêt-à-Porter : 9 Stratégies GagnantesStephany Gochuico
?
SOMMAIRE OMNICANAL
- Retail OmniCanal, c’est quoi ?
- Comportement des consommateurs OmniCanal
- 9 Stratégies Gagnantes pour l’OmniCanal
- Meilleures Enseignes "OmniCanal"
- Les plus Grandes Marques de Luxe dans le monde
- Les marques de Luxe, devraient-elles devenir "OmniCanal" ?
- Comportement des Consommateurs Fortunés
- Comparatif comportemental des différentes générations
- Comment réussir l’OmniCanal ?
End-to-End Deep Learning Deployment with ONNXNick Pentreath
?
The Open Neural Network Exchange (ONNX) standard has emerged for representing deep learning models in a standardized format. In this talk, I will discuss:
1. ONNX for exporting deep learning computation graphs, the ONNX-ML component of the specification for exporting both traditional ML models, common feature extraction, data transformation and post-processing steps.
2. How to use ONNX and the growing ecosystem of exporter libraries for common frameworks (including TensorFlow, PyTorch, Keras, scikit-learn and Apache SparkML) to deploy complete deep learning pipelines.
3. Best practices for working with and combining these disparate exporter toolkits, as well as highlight the gaps, issues, and missing pieces to be taken into account and still to be addressed.
Introduction to natural language processing (NLP)Alia Hamwi
?
The document provides an introduction to natural language processing (NLP). It defines NLP as a field of artificial intelligence devoted to creating computers that can use natural language as input and output. Some key NLP applications mentioned include data analysis of user-generated content, conversational agents, translation, classification, information retrieval, and summarization. The document also discusses various linguistic levels of analysis like phonology, morphology, syntax, and semantics that involve ambiguity challenges. Common NLP tasks like part-of-speech tagging, named entity recognition, parsing, and information extraction are described. Finally, the document outlines the typical steps in an NLP pipeline including data collection, text cleaning, preprocessing, feature engineering, modeling and evaluation.
This document discusses obfuscation and mutations in malware. It begins by defining obfuscation as deliberately creating code that is difficult for humans to understand. It then describes various obfuscation techniques used in malware including dead code insertion, register reassignment, and subroutine reordering. The document also discusses different types of malware like viruses, worms, and trojans. It classifies malware into first and second generation and describes techniques used in second generation malware like encryption, oligomorphism, polymorphism, and metamorphism. The document concludes by explaining various malware detection methods such as signature-based, behavior-based, heuristic, and hybrid approaches.
According to the last survey of the smile evaluation, 80% people are dissatisfied with their smile. If you are interested in improving your smile, Cerritos Dental Implant Center provides affordable price and give you a brighter, whiter smile in about an hour!
A bunch of MBA Books teach us how to write business plans, how sharing creates values, how team building brings ideas, how product differentiation lead to success and how innovation deliver new ear. But we can think like essentialism; we can put those MBA notes away! Simple shows you how.
《簡單思考》是一本僅適合平時?有在思考的中階主管的書籍。作者?的十項方針幾乎推翻商學院的思維,具參考價值,但不一定適用於每一個人。