PyTorch for Deep Learning and Natural Language Processing
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PyTorch is a Python library that supports accelerated GPU processing for Machine Learning and Deep Learning. In this tutorial, I will teach the basics of PyTorch from scratch. I will then explore how to use it for some ML projects such as Neural Networks, Multi-layer perceptrons (MLPs), Sentiment analysis with RNN, and Image Classification with CNN.
Introduction to Python for Digital Humanities and Computational Research
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This documentation contains introductory material on Python Programming for Digital Humanities and Computational Research. This can be a go-to material for a beginner trying to learn Python programming and for anyone wanting a Python refresher.
Useful R Packages for Data Science and Statistics
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This Udacity article listed the most frequently used R packages for data science and statistics. For each package, the article provided the link to its official documentation. It will be a great start point if you want to start your data science journey in R.
Samtools Documentation
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Samtools is a suite of programs for interacting with high-throughput sequencing data, especially in the SAM/BAM format. It offers various utilities for processing, analyzing, and managing sequence data generated from next-generation sequencing (NGS) experiments. Samtools is widely used in bioinformatics and genomics research for tasks such as read alignment, variant calling, and data manipulation.
Optimizing Research Workflows - A Documentation of Snakemake
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Snakemake is a powerful and versatile workflow management system that simplifies the creation, execution, and management of data analysis pipelines. It uses a user-friendly, Python-based language to define workflows, making it particularly valuable for automating and reproducibly managing complex computational tasks in research and data analysis.
Introductory Tutorial to Numpy and Pandas for Data Analysis
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In this tutorial, I present an overview with many examples of the use of Numpy and Pandas for data analysis. Beginners in the field of data analysis can find It incredibly helpful, and at the same time, anyone who already has experience in data analysis and needs a refresher can find value in it. I discuss the use of Numpy for analyzing 1D and 2D multidimensional data and an introduction on using Pandas to manipulate CSV files.
Pandas - Python
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pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. It lets you store data in easy to manage and display data frames, with column names and datatypes.
A survey on datasets for fairness-aware machine learning
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The research paper provides an overview of various datasets that have been used to study fairness in machine learning. It discusses the characteristics of these datasets, such as their size, diversity, and the fairness-related challenges they address. The paper also examines the different domains and applications covered by these datasets.
Fairness and Machine Learning
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The "Fairness and Machine Learning" book offers a rigorous exploration of fairness in ML and is suitable for researchers, practitioners, and anyone interested in understanding the complexities and implications of fairness in machine learning.
Intro to Statistical Computing with Stan
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The Stan language is used to specify a (Bayesian) statistical model with an imperative program calculating the log probability density function. Here are some useful links to start your exploration of this statistical programming language, and a Python interface to Stan.
What is fairness in ML?
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This article discusses the importance of fairness in machine learning and provides insights into how Google approaches fairness in their ML models.
The article covers several key topics:
Introduction to fairness in ML: It provides an overview of why fairness is essential in machine learning systems, the potential biases that can arise, and the impact of biased models on different communities.
Defining fairness: The article discusses various definitions of fairness, including individual fairness, group fairness, and disparate impact. It explains the challenges in achieving fairness due to trade-offs and the need for thoughtful considerations.
Addressing bias in training data: It explores how biases can be present in training data and offers strategies to identify and mitigate these biases. Techniques like data preprocessing, data augmentation, and synthetic data generation are discussed.
Fairness in ML algorithms: The article examines the potential biases that can arise from different machine learning algorithms, such as classification and recommendation systems. It highlights the importance of evaluating and monitoring models for fairness throughout their lifecycle.
Fairness tools and resources: It showcases various tools and resources available to practitioners and developers to help measure, understand, and mitigate bias in machine learning models. Google's TensorFlow Extended (TFX) and What-If Tool are mentioned as examples.
Google's approach to fairness: The article highlights Google's commitment to fairness and the steps they take to address fairness challenges in their ML models. It mentions the use of fairness indicators, ongoing research, and partnerships to advance fairness in AI.
Overall, the article provides a comprehensive overview of fairness in machine learning and offers insights into Google's approach to building fair ML models.