Entries by tag: machine-learning

Including child/synonym tags

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
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 ›

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
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  04 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  30 Sep 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 ›

SVM and Kernels: The very least every data scientist needs to know  25 Sep 2017    data-science machine-learning svm
Quick recap with main points on SVMs, where and how to use them and basic exmaples. 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
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    wip 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    wip neural nets
Tricks and Practical tips for training neural nets faster. Credit is mostly to Geoff Hinton and Yann LeCun. Read More ›