다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol−1) predictions in compositional and configurational chemical space for molecules of intermediate size. As an example of chemical relevance, the model reveals a classification of aromatic rings with respect to their stability. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems.

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Quantum-Chemical Insights from Deep Tensor Neural Networks

fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches. It aims to do both things without substantial compromises in ease of use, flexibility, or performance. This is possible thanks to a carefully layered architecture, which expresses common underlying patterns of many deep learning and data processing techniques in terms of decoupled abstractions. These abstractions can be expressed concisely and clearly by leveraging the dynamism of the underlying Python language and the flexibility of the PyTorch library. fastai includes: a new type dispatch system for Python along with a semantic type hierarchy for tensors; a GPU-optimized computer vision library which can be extended in pure Python; an optimizer which refactors out the common functionality of modern optimizers into two basic pieces, allowing optimization algorithms to be implemented in 4–5 lines of code; a novel 2-way callback system that can access any part of the data, model, or optimizer and change it at any point during training; a new data block API; and much more. We used this library to successfully create a complete deep learning course, which we were able to write more quickly than using previous approaches, and the code was more clear. The library is already in wide use in research, industry, and teaching.

Fastai: A Layered API for Deep Learning

DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsAbout This BookLeverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualizationLearn effective strategies and best practices to improve and optimize machine learning systems and algorithmsAsk and answer tough questions of your data with robust statistical models, built for a range of datasetsWho This Book Is ForIf you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.What You Will LearnExplore how to use different machine learning models to ask different questions of your dataLearn how to build neural networks using Keras and TheanoFind out how to write clean and elegant Python code that will optimize the strength of your algorithmsDiscover how to embed your machine learning model in a web application for increased accessibilityPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringOrganize data using effective pre-processing techniquesGet to grips with sentiment analysis to delve deeper into textual and social media dataIn DetailMachine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization.Style and approachPython Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.

Python Machine Learning

The correct use of model evaluation, model selection, and algorithm selection techniques is vital in academic machine learning research as well as in many industrial settings. This article reviews different techniques that can be used for each of these three subtasks and discusses the main advantages and disadvantages of each technique with references to theoretical and empirical studies. Further, recommendations are given to encourage best yet feasible practices in research and applications of machine learning. Common methods such as the holdout method for model evaluation and selection are covered, which are not recommended when working with small datasets. Different flavors of the bootstrap technique are introduced for estimating the uncertainty of performance estimates, as an alternative to confidence intervals via normal approximation if bootstrapping is computationally feasible. Common cross-validation techniques such as leave-one-out cross-validation and k-fold cross-validation are reviewed, the bias-variance trade-off for choosing k is discussed, and practical tips for the optimal choice of k are given based on empirical evidence. Different statistical tests for algorithm comparisons are presented, and strategies for dealing with multiple comparisons such as omnibus tests and multiple-comparison corrections are discussed. Finally, alternative methods for algorithm selection, such as the combined F-test 5x2 cross-validation and nested cross-validation, are recommended for comparing machine learning algorithms when datasets are small.

Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning

MLxtend is a library that implements a variety of core algorithms and utilities for machine learning and data mining. The primary goal of MLxtend is to make commonly used tools accessible to researchers in academia and data scientists in industries focussing on userfriendly and intuitive APIs and compatibility to existing machine learning libraries, such as scikit-learn, when appropriate. While MLxtend implements a large variety of functions, highlights include sequential feature selection algorithms (Pudil, Novovičová, and Kittler 1994), implementations of stacked generalization (Wolpert 1992) for classification and regression, and algorithms for frequent pattern mining (Agrawal and Ramakrishnan 1994). The sequential feature selection algorithms cover forward, backward, forward floating, and backward floating selection and leverage scikit-learn’s cross-validation API (Pedregosa et al. 2011) to ensure satisfactory generalization performance upon constructing and selecting feature subsets. Besides, visualization functions are provided that allow users to inspect the estimated predictive performance, including performance intervals, for different feature subsets. The ensemble methods in MLxtend cover majority voting, stacking, and stacked generalization, all of which are compatible with scikit-learn estimators and other libraries as XGBoost (Chen and Guestrin 2016). In addition to feature selection, classification, and regression algorithms, MLxtend implements model evaluation techniques for comparing the performance of two different models via McNemar’s test and multiple models via Cochran’s Q test. An implementation of the 5x2 cross-validated paired t-test (Dietterich 1998) allows users to compare the performance of machine learning algorithms to each other. Furthermore, different flavors of the Bootstrap method (Efron and Tibshirani 1994), such as the .632 Bootstrap method (Efron 1983) are implemented to compute confidence intervals of performance estimates. All in all, MLxtend provides a large variety of different utilities that build upon and extend the capabilities of Python’s scientific computing stack.

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MLxtend: Providing machine learning and data science utilities and extensions to Python's scientific computing stack

Table of Contents Giving Computers the Ability to Learn from Data Training Simple ML Algorithms for Classification ML Classifiers Using scikit-learn Building Good Training Datasets - Data Preprocessing Compressing Data via Dimensionality Reduction Best Practices for Model Evaluation and Hyperparameter Tuning Combining Different Models for Ensemble Learning Applying ML to Sentiment Analysis Embedding a ML Model into a Web Application Predicting Continuous Target Variables with Regression Analysis Working with Unlabeled Data - Clustering Analysis Implementing Multilayer Artificial Neural Networks Parallelizing Neural Network Training with TensorFlow TensorFlow Mechanics Classifying Images with Deep Convolutional Neural Networks Modeling Sequential Data Using Recurrent Neural Networks GANs for Synthesizing New Data RL for Decision Making in Complex Environments

Python machine learning : machine learning and deep learning with Python, scikit-learn, and TensorFlow

Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical machine learning and scalable general-purpose graphics processing unit (GPU) computing, have become critical components of artificial intelligence, enabling many of these astounding breakthroughs and lowering the barrier to adoption. Python continues to be the most preferred language for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and clean high-level APIs. This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it. We cover widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward.

Sebastian Raschka

Papers

Python Machine Learning

Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning

MLxtend: Providing machine learning and data science utilities and extensions to Python's scientific computing stack

Python machine learning : machine learning and deep learning with Python, scikit-learn, and TensorFlow

Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence