Data Science

Data Science | News, how-tos, features, reviews, and videos

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Semi-supervised learning explained

Using a machine learning model’s own predictions on unlabeled data to add to the labeled data set sometimes improves accuracy, but not always

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Deep learning frameworks: PyTorch vs. TensorFlow

If you actually need a deep learning model, PyTorch and TensorFlow are both good choices

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Automated machine learning or AutoML explained

AutoML frameworks and services eliminate the need for skilled data scientists to build machine learning and deep learning models

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Stop searching for that data scientist unicorn

Instead, focus on hiring the technical skills needed to build the team, and the soft skills needed to work on the team

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Applying devops in data science and machine learning

Having data scientists collaborate with devops and engineers leads to better business outcomes, but understanding their different requirements is key

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The best machine learning and deep learning libraries

TensorFlow, Spark MLlib, Scikit-learn, PyTorch, MXNet, and Keras shine for building and training machine learning and deep learning models

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Julia vs. Python: Which is best for data science?

Python has turned into a data science and machine learning mainstay, while Julia was built from the ground up to do the job

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TensorFlow 2 review: Easier machine learning

Now more platform than toolkit, TensorFlow has made strides in everything from ease of use to distributed training and deployment

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Supervised learning explained

Supervised learning turns labeled training data into a tuned predictive model

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What is TensorFlow? The machine learning library explained

TensorFlow is a Python-friendly open source library for numerical computation that makes machine learning faster and easier

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What’s new in TensorFlow 2.0

Google’s TensorFlow 2.0 is now available in beta, with a focus on improving performance, ease, compatibility, and continuity

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The best new features in Python 3.8

From a powerful new assignment syntax to under-the-hood overhauls, Python 3.8 steps toward a more modern Python codebase

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Reinforcement learning explained

Reinforcement learning uses rewards and penalties to teach computers how to play games and robots how to perform tasks independently

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Machine learning explained

Able to learn from data, machine learning algorithms can solve problems that are too complex to solve with conventional programming

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Machine learning algorithms explained

Machine learning uses algorithms to turn a data set into a model. Which algorithm works best depends on the problem

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What is Jupyter Notebook? Data analysis made easier

Jupyter Notebook combines live code, graphics, visualizations, and text in shareable notebooks that run in a web browser

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Mozilla brings Python data science to the browser

Pyodide project uses Emscripten and WebAssembly to run Python and its data science libraries in any major browser

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Review: Google Cloud AutoML is truly automated machine learning

Google’s AutoML lets you create customized deep learning models without any knowledge of data science or programming

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