Data Science & Machine Learning Newsletter #101

Posted on Fri 22 December 2017 in Data Science & Machine Learning Newsletter


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  • Python module to perform under sampling and over sampling with various techniques
    • "imbalanced-learn is a python package offering a number of re-sampling techniques commonly used in datasets showing strong between-class imbalance. It is compatible with scikit-learn and is part of scikit-learn-contrib projects."
  • On Machine Learning and Programming Languages
    • "While machine learning does not yet have a dedicated language, several efforts are effectively creating hidden new languages underneath a Python API (like TensorFlow) while others are reusing Python as a modelling language (like PyTorch). We’d like to ask – are new ML-tailored languages required, and if so, why? More importantly, what might the ideal ML language of the future look like?"
  • Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
    • "We evolve the weights of a DNN with a simple, gradient-free, population-based genetic algorithm (GA) and it performs well on hard deep RL problems, including Atari and humanoid locomotion. The Deep GA successfully evolves networks with over four million free parameters, the largest neural networks ever evolved with a traditional evolutionary algorithm."
  • MUSE - A library for Multilingual Unsupervised or Supervised word Embeddings
    • "MUSE is a Python library for multilingual word embeddings, whose goal is to provide the community with: *state-of-the-art multilingual word embeddings based on fastText, *large-scale high-quality bilingual dictionaries for training and evaluation"
  • Deep Learning Achievements Over the Past Year

    [caption id="" align="alignright" width="300"]dl_achivements
    Deep Learning Achievements Over the Past Year[/caption]

    • "Around Christmas time, our team decided to take stock of the recent achievements in deep learning over the past year (and a bit longer). We translated the article by a data scientist, Ed Tyantov, to tell you about the most significant developments that can affect our future."
    • How to Improve my ML Algorithm?
    • "You have worked for weeks on building your machine learning system and the performance is not something you are satisfied with. You think of multiple ways to improve your algorithm’s performance, viz. collect more data, add more hidden units, add more layers, change the network architecture, change the basic algorithm etc. But which one of these will give the best improvement on your system? You can either try them all, invest a lot of time and find out what works for you. OR! You can use the following tips from Ng’s experience"
  • TensorFlow for Short-Term Stocks Prediction

    • "In machine learning, a convolutional neural network (CNN, or ConvNet) is a class of neural networks that has successfully been applied to image recognition and analysis. In this project I've approached this class of models trying to apply it to stock market prediction, combining stock prices with sentiment analysis."
  • 5 Tricks When AB Testing Is Off The Table
    • "Here’s the good news: just because we can’t always AB test a major experience doesn’t mean we have to fly blind when it matters most. A range of econometric methods can illuminate the causal relationships at play, providing actionable insights for the path forward."