Tag

Machine Learning

Before You Model: planning and scoping

From Machine Learning Engineering in Action by Ben Wilson

Before we get into how successful planning phases for ML projects are undertaken, let’s go through a simulation of the genesis of a typical project at a company that doesn’t have an established or proven process for initiating ML work.

Fine-Tuning a Pre-Trained ResNet-50

From Transfer Learning in Action Dipanjan Sarkar and Raghav Bali

This article delves into tuning up a pre-trained ResNet-50 with one-cycle learning rate.

Managing Data Sources in Machine Learning

From Graph-Powered Machine Learning by Alessandro Negro

This article discusses managing data in graph-powered machine learning projects.

Creating a Bipartite Graph for a User-Item Dataset

By Graph-Powered Machine Learning Alessandro Negro

This article discusses creating a bigraph for a user-item dataset.

Setting Limits on Experimentation

This article talks about the need to carefully plan a machine learning project—before you start it!

Deep Transfer Learning for NLP with Transformers

From Transfer Learning for Natural Language Processing by Paul Azunre

In this article, we cover some representative deep transfer learning modeling architectures for NLP that rely on a recently popularized neural architecture – the transformer – for key functions.

Fun Uses for Word Vectors

In this video, Hobson shows you how to move words from inflammatory to less inflammatory context with the help of word vectors (Word2vec).

Training an SVM model in R with mlr

Hefin I. Rhys teaches you how to train, tune, and cross-validate a Support Vector Machine model using RStudio and the awesome mlr machine learning package.

Essential Tools for Deep Learning and Data Science

Learn the most important tools in the repertoire of a data scientist and machine learning practitioner – Principal Component Analysis (PCA), Singular Value Decomposition (SVD), and Latent Semantic Analysis (LSA) – with the help of Krishnendu Chaudhury.

Automatic Differentiation in Python and PyTorch

Deep dive with Carl Osipov into understanding automatic differentiation used by PyTorch autograd for deep learning

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