Press "Enter" to skip to content

Category: Machine Learning

An Intro to Key Word Analysis

Published 2022-08-12 by Kevin Feasel

Lewis Prince continues a series on natural language processing:

Here we are with part 2 of this blog series on web scraping and natural language processing (NLP). In the first part I discussed what web scraping was, why it’s done and how it can be done. In this part I will give you details on what NLP is at a high level, and then go into detail of an application of NLP called key word analysis (KWA).

Read on for a high-level overview of the topic and how to do it in Cognitive Services. But not the topic model—that’d be a different post.

Comments closed

What’s New in SynapseML

Published 2022-08-11 by Kevin Feasel

Nellie Gustafsson and Mark Hamilton share an update:

SynapseML is a massively scalable (feel free to spin up hundreds of machines!) machine learning library built on Apache Spark. SynapseML makes it easy to train production-ready models to solve problems from simple classification and regression to anomaly detection, translation, image analysis, speech to text, and just about any ML challenge you are facing.  Under the hood, SynapseML integrates a wide array of ML technologies such as LightGBM, Vowpal Wabbit, ONNX, and the Cognitive Services into a single easy to use API compatible with MLFlow. We know, we know, everyone hates when developers invent new APIs, but you can rest easy because SynapseML integrates cleanly into existing Spark ML APIs so you can embed models directly into existing pipelines. We strive to make SynapseML available to developers wherever they work, and the library is available in a variety of languages like Python, Scala, Java, R. As of this release SynapseML is also usable from .NET, C#, F#.

Saving the best language for last, I see. Click through for the list of updates.

Comments closed

Text Clustering with Python

Published 2022-07-28 by Kevin Feasel

Luke Menzies takes us through the gensim library:

An interesting branch of machine learning is Natural Language Processing (NLP). As the name suggests, it involves training machines to detect patterns in language using algorithms. It is quite often the case that NLP is referred to as text analytics. It is actually more impressive than that. It examines vectorised patterns which not only looks at the positioning of elements but what it means in context to neighbouring elements within the vector. In a nutshell, this technique can be extended beyond text to patterns of linguistics in general and even contextual patterns. Nevertheless, its primary use in the machine learning world is to analyse text.

This article will focus on an interesting application of NLP which involves the clustering of text. Clustering is a popular unsupervised machine learning technique used for segmentation or grouping of data. It is a very powerful tool that is used across a variety of industries. However, it is rare you hear of applying clustering to text. This can be achieved using NLP functions, combined with clustering algorithms that can handle non-Euclidian distances.

Read on for an overview of the process and an example of combining DBSCAN with word2vec to cluster phrases.

Comments closed

The Seedy Underbelly of Machine Learning Fitting

Published 2022-07-08 by Kevin Feasel

John Mount is not impressed with a fair amount of machine learning:

For this to actually happen we need the actual system to be in our concept space, a lot of training data, and an abundance of caution.

In practice what we see more and more is the training procedure in fact attacks the evaluation procedure. It doesn’t just improve the quality of the fit artifact, but through mere optimization accidentally exploits weaknesses in the measurement system itself. When this happens, fitting does the following.

In ML training, we often accidentally “teach to the test” by comparing models via test data, which over time selects for models which are better fits for the test data. As John notes, this can come two separate ways and if you don’t define your optimization strategy correctly, you can accidentally train models which optimize on non-realistic things. A classic example is the neural network which could pick out malignant tumors from non-malignant tumors not because of any property of the tumor itself but rather because the malignant tumor images all had rulers in them and the non-malignant images did not. Read the whole thing for a second pitfall you can hit when training models.

Comments closed

PHI De-Identification in Databricks with NLP

Published 2022-06-24 by Kevin Feasel

Amir Kermany, et al, share a set of notebooks:

John Snow Labs, the leader in Healthcare natural language processing (NLP), and Databricks are working together to help organizations process and analyze their text data at scale with a series of Solution Accelerator notebook templates for common NLP use cases. You can learn more about our partnership in our previous blog, Applying Natural Language Processing to Health Text at Scale.

To help organizations automate the removal of sensitive patient information, we built a joint Solution Accelerator for PHI removal that builds on top of the Databricks Lakehouse for Healthcare and Life Sciences. John Snow Labs provides two commercial extensions on top of the open-source Spark NLP library — both of which are useful for de-identification and anonymization tasks — that are used in this Accelerator:

This is a really interesting scenario.

Comments closed

Saving and Loading a Keras Model

Published 2022-06-23 by Kevin Feasel

Jason Brownlee made it to a savepoint in time:

Given that deep learning models can take hours, days and even weeks to train, it is important to know how to save and load them from disk.

In this post, you will discover how you can save your Keras models to file and load them up again to make predictions.

After reading this tutorial you will know:

– How to save model weights and model architecture in separate files.

– How to save model architecture in both YAML and JSON format.

– How to save model weights and architecture into a single file for later use.

Read on for an updated step-by-step tutorial.

Comments closed

Example Data Pre-Processing Activities

Published 2022-06-22 by Kevin Feasel

Aayush Srivastava takes us through some pre-processing activities in machine learning:

After selecting the raw data for ML training, the most important task is data pre-processing. In broad sense, data preprocessing will convert the selected data into a form we can work with or can feed to ML algorithms. We always need to preprocess our data so that it can be as per the expectation of machine learning algorithm

Read on for examples of pre-processing steps and how pre-processing differs from data cleaning.

Comments closed

Normalization Layers in Deep Learning Models

Published 2022-06-16 by Kevin Feasel

Zhe Ming Chng explains why data normalization matters in data science:

You’ve probably been told to standardize or normalize inputs to your model to improve performance. But what is normalization and how can we implement it easily in our deep learning models to improve performance? Normalizing our inputs aims to create a set of features that are on the same scale as each other, which we’ll explore more in this article.

Also, thinking about it, in neural networks, the output of each layer serves as the inputs into the next layer, so a natural question to ask is: If normalizing inputs to the model helps improve model performance, does standardizing the inputs into each layer help to improve model performance too?

Click through for the tutorial.

Comments closed

Visualizing SHAP Values in R with shapviz

Published 2022-06-14 by Kevin Feasel

Michael Mayer announces a new package:

SHAP (SHapley Additive exPlanations, Lundberg and Lee, 2017) is an ingenious way to study black box models. SHAP values decompose – as fair as possible – predictions into additive feature contributions.

When it comes to SHAP, the Python implementation is the de-facto standard. It not only offers many SHAP algorithms, but also provides beautiful plots. In R, the situation is a bit more confusing. Different packages contain implementations of SHAP algorithms

Read on to see how shapviz works, how to install it, and the types of visuals you can create from it.

Comments closed