Data Science
Data Science | News, how-tos, features, reviews, and videos
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
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
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
Supervised learning explained
Supervised learning turns labeled training data into a tuned predictive model
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
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
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
Reinforcement learning explained
Reinforcement learning uses rewards and penalties to teach computers how to play games and robots how to perform tasks independently
Machine learning explained
Able to learn from data, machine learning algorithms can solve problems that are too complex to solve with conventional programming
Machine learning algorithms explained
Machine learning uses algorithms to turn a data set into a model. Which algorithm works best depends on the problem
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
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
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
5 reasons data science belongs in the cloud
Why laptop-hugging data scientists and server-hugging machine learning practitioners should consider letting go
![Jupiter's Great Red Spot and its surroundings was obtained by Voyager 1 on Feb. 25, 1979 [NASA/JPL]](https://images.idgesg.net/images/article/2019/02/nasa_pia00014_jupiter_great_red_spot_obtained_by_voyager_1_1979_02_25_3x2-100787333-small.3x2.jpg)
Python tutorial: Get started with Jupyter Notebook
Learn how to combine Python code, freeform text, mathematical formulas, and graphics in an interactive, shareable notebook
Guided automation for machine learning
Implementing a web-based blueprint for semi-automated machine learning, using the open source Knime Analytics Platform
How to automate machine learning
KNIME’s blueprint for guided automation allows business analysts to train, test, and tune machine learning models via web browser
Review: MXNet deep learning shines with Gluon
With the addition of the high-level Gluon API, Apache MXNet rivals TensorFlow and PyTorch for developing deep learning models
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