Gideon Mendels

Authors: Ernesto Evgeniy Sanches Shayda (esanches@stanford.edu), Ilkyu Lee (lqlee@stanford.ed...

Authors: Rifath Rashid (rifath@stanford.edu) and A...

Authors: Drake Johnson (drakej@stanford.edu), Tim Ngo (ngotm@stanford.edu), Augusto Fernandez (afyrxr@stanford.edu) I...

  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 about: Challenges to be aware of when...

Authors: Danny Takeuchi (dtakeuch@stanford.edu), R...

  With data scientists and machine learning teams working on ambitious applications of AI, this industry is positioned to revolutionize the way society works and communicates. Join patent experts Milo Eadan a...

  Part 1 of a two part series The weight initialization technique you choose for your neural network can de...

By Ronny Huang, July 2019   Ronny Huang is a postdoc student at The University of Maryland and researcher in residence at Ernst and Young. His research is primarily focu...

  This tutorial is Part II of a series. See Part I here. Successfully executing machine learni...

  Common issues, challenges, and solutions For those that missed our webinar last week, you can access the recording to learn: Opportunities to improve machine learnin...

How to use Ludwig and Comet.ml together to build powerful deep learning models right in your command line — using an example text classification model   Ludwig is a Tens...

  NEW YORK, NY — Comet.ml, the industry-leading meta machine learning platform, announced today their joint efforts with Uber AI on extending Ludwig, a low code deep learning toolbox, to suppo...

The rush to build and deploy machine learning models has exposed cracks in traditional DevOps processes.  ...

  Figure from Variational Sparse Coding Paper. Full paper here Interested in learning more about the Reproducibility Challenge? Read our kick-off piece here The third...

  Author: Jeremy Jordan Originally published at https://www.jeremyjordan.me on September 1, 2018, and was updated recently to reflect new resources. The goal of th...

Classifying fruits using a Keras multi-class image classification model and Google Open Images   ...

Reproducing the paper ‘Learning Neural PDE Solvers with Convergence Guarantees’   Results of a hyperparameter search in order to find the optimal number of layers and l...

We interview four teams with a mission to reproduce the latest in machine learning research as part of the 2019 ICLR Reproducibility Challenge.   Reproducibility is a pow...

  Pre-trained models are easy to use, but are you glossing over details that could impact your model performance? How many times have you run the following snippets: import to...

Testing OpenAI’s GPT-2 text generator model to write the next killer blog post   A snapshot an interactive visualization of a generator and discriminator interacting from GAN Lab ...

  Photo by Samuel Zeller on Unsplash Note: this article originally appeared on Oscar Chang’s website at https://crazyoscarchang.github.io/2019/02/16/seven-myths-in-machine-learning-r...

  Note: this article originally appeared on Raúl Gómez’s website at https://gombru.gith...

  Programmatically access your machine learning system of record Accessing your model weights, metrics, hyperparameters, images, and other workflow artifacts should be easy for 10...

  Comet.ml allows you to automatically track your machine learning code, experiments, hyperparame...

Our experience hosting New York data scientists and researchers from academia and industry   Our speaker Melanie Weber, Columbia PhD candidate, explains how she and her team deve...

In this tutorial, we will illustrate how to build an image recognition model using a convolutional neural network (CNN) i...

  Learn how to build and train a deep learning network to recognize numbers (MNIST), how to convert it in the...

Tracking and saving your model results just got that much easier with Comet.ml   For many data scientists, Jupyter notebooks have become the tool of choice. Its ability ...

See how companies like Uber and ZocDoc use machine learning to improve key business metrics   Ramping up for the keynotes at Strata Data Conference in New York — ph...

Compare across your model iterations efficiently with rich visualizations to identify your champion model   At Comet.ml, we believe that machine learning should be highly iterative, ...

  The latest episode of the In Context podcast is a must-listen for anyone in the machine learning space...

  Cross-validation is a technique used to measure and evaluate machine learning models performance. During training we create a number of partitions of the training set and train/tes...

Now you can easily find and organize your experiments with filtered views based on experiment metrics, metadata, and parameters   Machine learning teams often work with many, many models and t...

  So your model is finally done running, you’ve tweaked and optimized all of the hyperparameters you could to obtain the best results, and you’re ready to present your findings. Now what? ...

  At Comet.ml, we strive to help data scientists and machine learning engineers speed up the development and productionisation of their machine learning models. A key part of the ma...

This post by Yoel Zeldes is originally from his blog Another Datum and was reposted with his permission. Yoel is an algorithm engineer at Taboola.   Last week I had the opportunity to attend at ...

I’m very excited to announce that I’ve joined Comet.ml as Product Lead! After graduation, I became a product manager for Watson Studio, a powerful end to end machine learning platform. This ...