• AI, Data Science & Machine Learning Resources
      Pandas ebook
      Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python
      CNN Visualization
      Convolutional Neural Network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery.
      50 years of Data Science
      More than 50 years ago, John Tukey called for a reformation of academic statistics. In `The Future of Data Analysis', he pointed to the existence of an as-yet unrecognized science, whose subject of interest was learning from data, or `data analysis'.
      YOLOv3: An Incremental Improvement
      I managed to make some improvements to YOLO. But, honestly, nothing like super interesting, just a bunch of small changes that make it better. I also helped out with other people’s research a little.
      Model Evaluation, Model Selection, and Algorithm Selection
      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.
      Financial Market Time Series Prediction with Recurrent Neural Networks
      Learning from past history is a fudamentality ill-posed. A model may fit past data well but not perform well when presented with new inputs. With recurrent neural networks (RNNs), we leverage the modeling abilities of neural networks (NNs) for time series forecastings.
      Batch Normalization: Accelerating Deep Network Training
      Training Deep Neural Networks is complicated by the fact that the distribution of each layer’s inputs changes during training, as the parameters of the previous layers change.
      Python numpy reshape and stack cheatsheet
      Python numpy reshape and stack cheatsheet is a good resource for quick reference of most commonly used methods and functions
      U-Net: Convolutional Networks for Image Segmentation
      There is large consent that successful training of deep net- works requires many thousand annotated training samples. In this pa- per, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently.
      Xception: Deep Learning with Depthwise Separable Convolutions
      Convolutional neural networks have emerged as the master algorithm in computer vision in recent years, and developing recipes for designing them has been a subject of considerable attention.
      Important probability distributions
      Certain probability distributions occur with such regularity in real-life applications that they have been given their own names. Here, we survey and study basic properties of some of them.
      Methods for Detecting Outliers in Univariate Data Sets
      Most real-world data sets contain outliers that have unusually large or small values when compared with others in the data set. Outliers may cause a negative effect on data analyses, such as ANOVA and regression, based on distribution assumptions, or may provide useful information about data when we look into an unusual response to a given study.
      Ray: A Distributed Execution Engine for the Machine Learning Ecosystem
      Ray is an open source framework for parallel and distributed Python that can make numeric computations many times faster. Ray takes the existing concepts of functions and classes and translates them to the distributed setting as tasks and actors.

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