Why software engineering processes and tools don’t work for machine learning

Machine Learningby Nikolas LaskarisPosted onDecember 4, 2019December 6, 2019

While AI may be the new electricity significant challenges remain to realize AI potential. Here we examine why data scientists and teams can’t rely on software engineering tools and processes for machine learning.

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Webinar: Experiment Management – Rethink your ML Workflow

Webinarsby Nikolas LaskarisPosted onDecember 4, 2019December 4, 2019

Running machine learning initiatives is difficult. Why? It is not possible for data scientists and teams to manage reproducibility, loss of IP, visibility and tracking…

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How to apply machine learning and deep learning methods to audio analysis

Machine Learningby Nikolas LaskarisPosted onNovember 18, 2019December 5, 2019

To view the code, training visualizations, and more information about the python example at the end of this post, visit the Comet project page.  Introduction…

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Stanford Research Series: Grand Digital Piano: Multimodal Transfer of Learning of Sound and Touch

Academic Researchby Gideon MendelsPosted onOctober 24, 2019November 25, 2019

Authors: Ernesto Evgeniy Sanches Shayda (esanches@stanford.edu), Ilkyu Lee (lqlee@stanford.edu) I. MOTIVATION Musical instruments have evolved during thousands of years allowing performers to produce almost all…

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Estimating Uncertainty in Machine Learning Models — Part 3

Machine Learningby Dhruv NairPosted onOctober 18, 2019December 5, 2019

Check out part 1 (here)and part 2 (here) of this series In the last part of our series on uncertainty estimation, we addressed the limitations…

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Stanford Research Series: Making Trading Great Again: Trump-based Stock Predictions via doc2vec Embeddings

Academic Researchby Gideon MendelsPosted onOctober 2, 2019November 25, 2019

Authors: Rifath Rashid (rifath@stanford.edu) and Anton de Leon (aadeleon@atanford.edu) 1 Introduction The question of how accurately Twitter posts can model the movement of financial securities…

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Estimating Uncertainty in Machine Learning Models – Part 2

Machine Learningby Dhruv NairPosted onSeptember 26, 2019November 25, 2019

You can check out part 1 of this series here In part 1 of this series, we discussed the sources of uncertainty in machine learning models,…

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Stanford Research Series: Climate Classification Using Landscape Images

Academic Researchby Gideon MendelsPosted onSeptember 25, 2019November 26, 2019

Authors: Drake Johnson (drakej@stanford.edu), Tim Ngo (ngotm@stanford.edu), Augusto Fernandez (afyrxr@stanford.edu) I. Introduction: Recently, there has been an increase in interest in the public for the…

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Webinar Recording: Patenting AI

Productby Gideon MendelsPosted onSeptember 20, 2019November 26, 2019

Hosted by: Comet.ml and Pearl Cohen This week, we hosted a webinar with the patent experts at Pearl Cohen. During the webinar, Pearl Cohen talks…

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Stanford Research Series: Exploring Model Architectures and View-Specific Models for Chest Radiograph Diagnoses

Academic Researchby Gideon MendelsPosted onSeptember 19, 2019November 26, 2019

Authors: Danny Takeuchi (dtakeuch@stanford.edu), Raymond Thai (raythai@stanford.edu), Kevin Tran (ktran23@stanford.edu) This project tackles several current issues with automated chest X-ray radiography, specifically regarding work on…

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Popular Posts

  • How to apply machine learning and deep learning methods to audio analysis by Nikolas Laskaris | posted on November 18, 2019
  • Why software engineering processes and tools don't work for machine learning by Nikolas Laskaris | posted on December 4, 2019
  • Estimating Uncertainty in Machine Learning Models — Part 1 by Dhruv Nair | posted on September 12, 2019
  • Stanford Research Series: Grand Digital Piano: Multimodal Transfer of Learning of Sound and Touch by Gideon Mendels | posted on October 24, 2019
  • Estimating Uncertainty in Machine Learning Models — Part 3 by Dhruv Nair | posted on October 18, 2019

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