Manifold Blog

Manifold Blog

Using Dask in Machine Learning: Preprocessing

Posted by Jason Carpenter on Apr 25, 2019 6:00:00 AM

Introduction

This is the second post in a five part series about using Dask in machine learning workflows:

  • Using Dask in Machine Learning: Best Practices
  • Using Dask in Machine Learning: Preprocessing
  • Using Dask in Machine Learning: Feature Engineering
  • Using Dask in Machine Learning: Model Training
  • Using Dask in Machine Learning: Model Evaluation

Starting with this post, each installment will have data snapshots and code snippets to give you an example of the problem we are working on. We have this public self-contained GitHub repo. You can pull that repo and run the code yourself and follow along more closely.

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Topics: Data engineering, Machine learning

Using Dask in Machine Learning: Best Practices

Posted by Jason Carpenter on Jan 31, 2019 6:00:00 AM

Introduction

The Python ecosystem offers a number of incredibly useful open source tools for data scientists and machine learning (ML) practitioners. One such tool is Dask, available from Anaconda. At Manifold, we have used Dask extensively to build scalable ML pipelines.

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Topics: Data science, Data engineering, Machine learning

How to Get Started with Machine Learning — For Executives and Engineers

Posted by Martin Davy on Jan 10, 2019 7:00:00 AM

The machine learning (ML) space can seem intimidating — where do you start, and how does it all connect? At Manifold, we've compiled a list of resources borne of our expertise that we think will help you form a strong ML foundation. We hope to continue updating and improving this list, and may reshare it out periodically in the hopes that those who embark on this journey can have an even smoother and more fulfilling experience.

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Topics: Machine learning

Exploration vs. Exploitation in Reinforcement Learning

Posted by Rajendra Koppula on Jan 8, 2019 7:00:00 AM

Introduction

The last five years have seen many new developments in reinforcement learning (RL), a very interesting sub-field of machine learning (ML). Publication of "Deep Q-Networks" from DeepMind, in particular, ushered in a new era. As RL comes into its own, it's becoming clear that a key concept in all RL algorithms is the tradeoff between exploration and exploitation. In this post, we will simulate a problem called the "multi-armed bandit" in order to understand the details of this tradeoff. 

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Topics: Machine learning

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We publish occasional blog posts about our client work, open source projects, and conference experiences. We focus on industry insights and practical takeaways to help you accelerate your data roadmap and create business value.


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