Entries by tag: machine-learning

Including child/synonym tags

Paper Summary: Long Short-Term Memory-Networks for Machine Reading  25 Dec 2022    paper-summary attention sequence-learning
Summary of the 2016 article "Long Short-Term Memory-Networks for Machine Reading" by Cheng et al. AKA the "Self-attention" article Read More ›

Paper Summary: Identifying Mislabeled Instances in Classification Datasets  28 Jun 2021    paper-summary machine-learning-engineering machine-learning
Summary of the 2019 article "Identifying Mislabeled Instances in Classification Datasets" by Mueller and Markert. Read More ›

Paper Summary: Attention is All you Need  27 Jun 2020    paper-summary sequence-learning attention transformer-architecture
Summary of the 2017 article "Attention is All you Need" by Vaswani et al. Read More ›

Paper Summary: Hidden Technical Debt in Machine Learning Systems  23 Mar 2020    paper-summary machine-learning-engineering technical-debt
Summary of the 2015 article "Hidden Technical Debt in Machine Learning Systems" by Sculley et al. Read More ›

Scikit-learn Pipelines: Custom Transformers and Pandas integration  08 Mar 2020    pandas scikit-learn
Examples and reference on how to write customer transformers and how to create a single sklearn pipeline including both preprocessing steps and classifiers at the end, in a way that enables you to use pandas dataframes directly in a call to fit. Read More ›

Paper Summary: Software Engineering for Machine Learning: A Case Study  25 Jan 2020    paper-summary machine-learning-engineering software-engineering
Summary of the 2019 article "Software Engineering for Machine Learning: A Case Study" by Amershi et al. Read More ›

Paper Summary: Neural Machine Translation by Jointly Learning to Align and Translate  11 Jan 2020    paper-summary attention sequence-learning machine-translation
Summary of the 2014 article "Neural Machine Translation by Jointly Learning to Align and Translate" by Bahdanau et al. Read More ›

Paper Summary: Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift  23 Dec 2019    paper-summary machine-learning-engineering
Summary of the 2019 article "Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift" by Rabanser et al. Read More ›

Paper Summary: Long Short-Term Memory  16 Nov 2019    paper-summary neural-networks sequence-learning
Summary of the 1997 article "Long Short-Term Memory" by Hochreiter and Schmidhuber. Read More ›

Paper Summary: 150 Successful Machine Learning Models: 6 Lessons Learned at Booking.com  09 Nov 2019    paper-summary machine-learning-engineering
Summary of the 2019 article "150 Successful Machine Learning Models: 6 Lessons Learned at Booking.com" by Bernardi et al. Read More ›

Choosing C Hyperparameter for SVM Classifiers: Examples with Scikit-Learn  20 Jun 2019    scikit-learn svm
Analysis of the effect of the C parameter on learning SVM models under a noisy data regime. With examples using the Python Library Scikit-learn. Read More ›

Michelangelo Palette Overview  08 Jun 2019    machine-learning-engineering
Overview of Palette, the feature store system that is part of Uber's Michelangelo Mahcine Learning Platform. Based off the talk given at qcon.ai. Read More ›

Paper Summary: Scaling Distributed Machine Learning with the Parameter Server  25 May 2019    paper-summary machine-learning-engineering distributed-computing
Summary of the 2014 article "Scaling Distributed Machine Learning with the Parameter Server" by Li et al. Read More ›

Evaluation Metrics for Ranking problems: Introduction and Examples  24 Jan 2019    machine-learning model-evaluation
Explanation and examples on how to calculate the performance of ranked predictions for machine learning. Read More ›

Paper Summary: The Tradeoffs of Large Scale Learning  15 Dec 2018    paper-summary machine-learning
Summary of the 2007 article "The Tradeoffs of Large Scale Learning" by Bottou and Bousquet. Read More ›

Quick Summary + Thoughts on BigHead: AirBNB's ML Platform  03 Dec 2018    ml-platforms
Notes on AirBNB's Bighead ML platform, based off videos and presentations. Read More ›

Thoughts on Michelangelo: Uber's Machine Learning Platform  20 Nov 2018    machine-learning-platforms
Reading and dissecting the way Uber does Machine Learning. Read More ›

Paper Summary: Statistical Modeling: The Two Cultures  02 Nov 2018    paper-summary machine-learning
Summary of the 2001 article "Statistical Modeling: The Two Cultures" by Leo Breiman. Read More ›

Heads-up for Deploying Scikit-learn Models to Production: Quick Checklist  01 Sep 2018    scikit-learn production machine-learning-engineering
A couple of tips for addressing common problems and unexpected situations when using scikit-learn models in production.. Read More ›

Cross-Validation Examples with Scikit-Learn  01 Sep 2018    scikit-learn
Using cross-validation within scikit-learn. Read More ›

Example Project Template: Serve a Scikit-learn Model via a Flask API  27 Jun 2018    flask scikit-learn
Full (albeit simple) example on how to create a simple Flask API to serve predictions using a pre-trained scikit-learn model. Includes supporting features such as logging, error handling, input validation, etc. Full code available on Github. Read More ›

Evaluation Metrics for Regression Problems: Quick examples + Reference  26 May 2018    machine-learning metrics
Regression problems are evaluated against specific metrics that analyze whether the residuals (difference between actual and predicted values) indicate that a fitted model is a good fit for the data. Here are some of the most commonly-used metrics in that domain. Read More ›

Scikit-Learn examples: Making Dummy Datasets  02 May 2018    scikit-learn
Make dummy datasets to test out classifiers and/or parameter configurations in Scikit-learn. Read More ›

Podcast Episode Overview: What Machine Learning Engineers need to Know  23 Apr 2018    data-science peopleware data-newsletter-5 machine-learning-engineering
Overview of a great podcast episode on how much (if at all) we need a new role for data teams, namely Machine Learning Engineers. Read More ›

Visualizing Machine Learning Models: Examples with Scikit-learn, XGB and Matplotlib  23 Apr 2018    matplotlib machine-learning scikit-learn
Examples on how to use matplotlib and Scikit-learn together to visualize the behaviour of machine learning models, conduct exploratory analysis, etc. Read More ›

Introduction to AUC and Calibrated Models with Examples using Scikit-Learn  15 Apr 2018    machine-learning data-science model-evaluation
Inspired by a podcast episode by Linear Digressions, which talks about what AUC is and what it is not and why you need well calibrated models if you want to treat their outputs as probabilities. Read More ›

Gaussian Processes for Classification and Regression: Introduction and Usage  19 Nov 2017    machine-learning statistics
Study guide for understanding Gaussian Processes (also Sparse Gaussian Processes) as applied to classification in machine learning. Read More ›

Scikit-Learn Pipeline Examples  21 Oct 2017    scikit-learn
Examples of how to use classifier pipelines on Scikit-learn. Includes examples on cross-validation regular classifiers, meta classifiers such as one-vs-rest and also keras models using the scikit-learn wrappers. Read More ›

Paper Summary: Recursive Neural Language Architecture for Tag Prediction  05 Oct 2017    paper-summary tags neural-nets embeddings
Summary of the 2016 article "Recursive Neural Language Architecture for Tag Prediction" by Kataria. Read More ›

Paper Summary: Translating Embeddings for Modeling Multi-relational Data  01 Oct 2017    embeddings structure paper-summary neural-networks
Summary of the 2013 article "Translating Embeddings for Modeling Multi-relational Data" by Bordes et al. Read More ›

A Quick Summary of Ensemble Learning Strategies  01 Sep 2017    data-newsletter-4 machine-learning
Ensemble learning refers to mixing the outputs of several classifiers in various ways, so as to get a better result than each classifier individually. Read More ›

Evaluation Metrics for Classification Problems: Quick Examples + References  31 Aug 2017    data-newsletter-4 machine-learning model-evaluation
There are multiple ways to measure your model's performance in machine learning, depending upon what objectives you have in mind. Some of the most important are Accuracy, Precision, Recall, F1 and AUC. Read More ›

Machine Learning and Data Science: Generally Applicable Tips and Tricks  18 May 2017    machine-learning data-science best-practices
A couple of general, practical tips and tricks that may be used when dealing with data science and/or machine learning problems. Read More ›

Scikit-Learn Cheatsheet: Reference and Examples  10 Mar 2017    scikit-learn
Just a couple of things you may find yourself doing over and over again when working with scikit-learn. Read More ›

Tricks for Training Neural Nets Faster  20 Feb 2017    neural-nets performance
Tricks and Practical tips for training neural nets faster. Credit is mostly to Geoff Hinton and Yann LeCun. Read More ›