# NumPy Open-source Python package for numerical computing. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Lambda SnapStart Comes to Container Images, Ending a Packaging Tradeoff DevFeed: [Lambda SnapStart Comes to Container Images, Ending a Packaging Tradeoff]() Original publisher: [Read original article]() Author: Steef-Jan Wiggers Published: 2026-09-12T10:09:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Deployment](), [NumPy]() Tags: [announcement](), [architecture](), [architecture-design](), [aws](), [aws-lambda](), [cloud](), [containers](), [development](), [devops](), [lambda-snapstart-container-image](), [news](), [performance](), [python](), [serverless](), [startup]() ### AI overview AWS has extended Lambda SnapStart to container-image functions, allowing teams to combine larger dependency packages with sub-second startup times. The change removes a packaging tradeoff previously faced by workloads such as pandas- and NumPy-based Lambda functions. ### Source excerpt AWS has extended Lambda SnapStart to container image functions, which hold up to 10 GB against 250 MB for zip archives. Teams previously chose between dependency headroom and sub-second startup. A Reddit thread from a month earlier shows what that cost: stripping whitespace and docstrings from installed packages to stay under the limit. By Steef-Jan Wiggers ## The Real Python Podcast - Episode #307: Improving NumPy Performance on Free-Threaded Python DevFeed: [The Real Python Podcast - Episode #307: Improving NumPy Performance on Free-Threaded Python]() Original publisher: [Read original article]() Author: Real Python Published: 2026-08-14T12:00:00Z Content type: article Language: en Sources: [Real Python]() Topics: [NumPy](), [Python](), [Data visualization](), [Django](), [JSON]() Tags: [articles](), [news](), [performance](), [podcast](), [python]() ### AI overview Episode 307 discusses the work required to improve NumPy performance on free-threaded Python. It explains that removing the Global Interpreter Lock alone was insufficient, and that profiling exposed hidden bottlenecks in NumPy and CPython. The episode also covers Python community news, releases, PyPI announcements, Django tools, JSON serialization, data visualization, imports, and object-oriented Python. ### Source excerpt What bottlenecks were preventing NumPy from scaling on free-threaded Python? Christopher Trudeau is back on the show this week with another batch of PyCoder's Weekly articles and projects. ## Run High-Performance Core Math at Scale with NVIDIA nvmath-python DevFeed: [Run High-Performance Core Math at Scale with NVIDIA nvmath-python]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-07-30T22:43:04Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Python](), [CUDA](), [math](), [GPU](), [NumPy](), [cpu](), [PyTorch](), [pip]() Tags: [agentic-ai-generative-ai](), [c-plus-plus](), [ci-cd](), [cpu](), [cuda](), [cuda-x](), [data-science](), [developer-tools-techniques](), [device-apis](), [featured](), [gpu](), [multi-gpu](), [nvidia](), [performance](), [python](), [pytorch](), [scale]() ### AI overview NVIDIA nvmath-python 1.0 provides a Pythonic interface to CUDA-X and NVPL math libraries, enabling optimized numerical operations on CPUs, CUDA GPUs, and distributed multi-GPU, multi-node systems. The article covers its sparse-tensor approach, flexible installation options, and interoperability with NumPy, CuPy, and PyTorch. ### Source excerpt NVIDIA nvmath-python is a library designed to bridge the gap between the Python scientific community and NVIDIA CUDA-X math libraries. It gives Python users... ## WWDC 2026 Viewing Guide DevFeed: [WWDC 2026 Viewing Guide]() Original publisher: [Read original article]() Author: Keith Harrison Published: 2026-06-15T09:16:45Z Content type: tutorial Language: en Sources: [K. Harrison]() Topics: [Swift](), [SwiftUI](), [Xcode](), [WebAssembly](), [gRPC](), [MLX](), [NumPy](), [Testing]() Tags: [2026](), [apple](), [auto-layout](), [comparison](), [ios](), [ios27](), [macos27](), [objective-c](), [swift](), [swiftui](), [webassembly](), [wwdc](), [xcode]() ### AI overview A guide to planning and watching WWDC 2026 sessions, highlighting platform themes and selected updates in Swift, SwiftUI, Xcode, WebAssembly, gRPC, and MLX. ### Source excerpt My WWDC 2026 viewing guide to help you plan the sessions you want to watch. Where Do I Start? The two big themes for the year are again Liquid Glass and Apple Intelligence. Start by watching the Platforms State of the Union (SOTU) for a summary of what's new this year. If you only have five minutes try the SOTU recap. You don't have to learn everything new today! There are over 100 sessions and a lot of new stuff to learn. You may feel like you're getting left behind. Don't let it overwhelm you. You've got time. Don't burn yourself out trying to keep up. Watching The Sessions I watch the videos using the Apple Developer app. It's available on macOS and iOS. The video player supports from 0.5x to 2x playback, most videos have transcripts and summaries, and allow copying of the onscreen sample code. You can also watch on YouTube. There are a lot of sessions, but many are short (15-20 minutes). There's no padding and Apple's engineers get to the point quickly. Swift Swift 6.4 is here with some quality of life improvements: What's new in Swift Condense long @available attributes with anyAppleOS, @diagnose to control compiler deprecation warnings. Double-colon module selector. Standard Library task cancellation shield, dictionary mapKeyedValues, new FilePath type. XCTest interop with Swift Testing. Subprocess 1.0. New ProgressManager. @C attribute for Swift-C interop. Compile to WebAssembly. New @inline(always) and @specialized attributes. Iterable protocol for noncopyable elements. Build real-time apps and services with gRPC and Swift gRPC Swift package to generate and deploy network code for remote procedure call services. Explore numerical computing in Swift with MLX NumPy-like API. Examples of computing the Mandlebrot set, heat distribution, and curve fitting. SwiftUI Liquid Glass changes and some welcome improvements: What's new in SwiftUI Liquid Glass is now mandatory. Minimal menu icons by default. Resize live previews in Xcode. Prominent tab role. Toolbar visibil ## Coding Challenge #119 - AI Pong Player DevFeed: [Coding Challenge #119 - AI Pong Player]() Original publisher: [Read original article]() Author: John Crickett Published: 2026-05-09T08:01:53Z Content type: tutorial Language: en Sources: [Coding Challenges]() Topics: [Code Challenge](), [Reinforcement learning](), [coding](), [Deep neural networks](), [NumPy]() Tags: [ai](), [coding](), [games](), [neural-networks](), [python](), [reinforcement-learning]() ### AI overview A coding challenge guides readers through building a reinforcement learning agent that learns to play Atari Pong from raw screen pixels. It introduces policy gradients, the REINFORCE algorithm, returns, stochastic policies, and variance reduction, with Python and NumPy as prerequisites. ### Source excerpt This challenge is to build your own AI pong player with reinforcement learning. ## What's new in TensorFlow 2.18 DevFeed: [What's new in TensorFlow 2.18]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2024-10-28T19:00:00Z Content type: release Language: en Sources: [The TensorFlow Blog]() Topics: [Tensorflow](), [releases](), [LiteRT](), [NumPy](), [CUDA](), [cudnn](), [NCCL](), [Google]() Tags: [announcement](), [cuda](), [cudnn](), [gpu](), [litert](), [migration](), [nccl](), [nvidia](), [nvidia-rtx](), [python](), [release](), [releases](), [reproducible-builds](), [tensorflow](), [tensorflow-core]() ### AI overview TensorFlow 2.18 introduces NumPy 2.0 compatibility updates, transitions TFLite development to the LiteRT repository, and adds hermetic CUDA, cuDNN, and NCCL dependencies for more reproducible source builds. Binary packages add dedicated kernels for compute capability 8.9 GPUs, including NVIDIA RTX 40 series, L4, and L40, while dropping precompiled support for Maxwell GPUs. ### Source excerpt Posted by the TensorFlow team TensorFlow 2.18 has been released! Highlights of this release (and 2.17) include NumPy 2.0, LiteRT repository, CUDA Update, Hermetic CUDA and more. For the full release notes, please click here. Note: Release updates on the new multi-backend Keras will be published on keras.io, starting with Keras 3.0. For more information, please see https://keras.io/keras_3/. TensorFlow Core NumPy 2.0 The upcoming TensorFlow 2.18 release will include support for NumPy 2.0. While the majority of TensorFlow APIs will function seamlessly with NumPy 2.0, this may break some edge cases of usage, e.g., out-of-boundary conversion errors and numpy scalar representation errors. You can consult the following common solutions. Note that NumPy's type promotion rules have been changed (See NEP 50 for details). This may change the precision at which computations happen, leading either to type errors or to numerical changes to results. Please see the NumPy 2 migration guide. We've updated some TensorFlow tensor APIs to maintain compatibility with NumPy 2.0 while preserving the out-of-boundary conversion behavior in NumPy 1.x. LiteRT Repository We're making some changes to how LiteRT (formerly known as TFLite) is developed. Over the coming months, we'll be gradually transitioning TFLite's codebase to LiteRT. Once the migration is complete, we'll start accepting contributions directly through the LiteRT repository. There will no longer be any binary TFLite releases and developers should switch to LiteRT for the latest updates. Hermetic CUDA If you build TensorFlow from source, Bazel will now download specific versions of CUDA, CUDNN and NCCL distributions, and then use those tools as dependencies in various Bazel targets. This enables more reproducible builds for Google ML projects and supported CUDA versions because the build no longer relies on the locally installed versions. More details are provided here. CUDA Update TensorFlow binary distributions now ship with d ## Converting Vertex-Colored Meshes to Textured Meshes DevFeed: [Converting Vertex-Colored Meshes to Textured Meshes]() Original publisher: [Read original article]() Author: Dylan Ebert Published: 2024-09-30T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [3D](), [Image processing](), [Library](), [OpenCV](), [NumPy]() Tags: [3d](), [code](), [image-processing](), [library](), [mesh](), [tutorial](), [vision]() ### AI overview A tutorial explaining how to convert vertex-colored meshes into UV-mapped, textured meshes. It presents a quick solution using the InstantTexture library and a detailed implementation based on UV generation, vertex-color remapping, barycentric interpolation, and texture-buffer filling. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Polynomial Multiplication Using the FFT DevFeed: [Polynomial Multiplication Using the FFT]() Original publisher: [Read original article]() Published: 2022-11-16T08:00:00Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [polynomials](), [Algorithm](), [NumPy](), [Mathematics]() Tags: [algorithm](), [arrays](), [efficiently](), [fft](), [fourier-transform](), [mathematics](), [polynomial-interpolation](), [polynomials](), [programming](), [python]() ### AI overview This tutorial explains how to multiply two polynomials efficiently using the Fast Fourier Transform. It contrasts the naive O(n^2) approach with polynomial interpolation, pointwise multiplication, and carefully chosen roots of unity that enable reusable computations. ### Source excerpt Problem: Compute the product of two polynomials efficiently. Solution: import numpy from numpy.fft import fft, ifft def poly_mul(p1, p2): """Multiply two polynomials. p1 and p2 are arrays of coefficients in degree-increasing order. """ deg1 = p1.shape[0] - 1 deg2 = p1.shape[0] - 1 # Would be 2*(deg1 + deg2) + 1, but the next-power-of-2 handles the +1 total_num_pts = 2 * (deg1 + deg2) next_power_of_2 = 1 << (total_num_pts - 1). ## Google Brain's Jax and Flax DevFeed: [Google Brain's Jax and Flax]() Original publisher: [Read original article]() Published: 2021-01-15T13:59:26Z Content type: opinion Language: en Sources: [Lambda the Ultimate]() Topics: [Artificial Intelligence](), [Deep learning](), [Google](), [Algorithms](), [NumPy](), [Python](), [Optimization]() Tags: [ai](), [algorithms](), [array](), [customization](), [deep-learning](), [google](), [implementation](), [optimization](), [python](), [scientific-programming](), [software-engineering]() ### AI overview The article introduces Google Brain's JAX and Flax alongside TensorFlow. It describes JAX as a higher-level language for specifying deep learning algorithms, combining Autograd and XLA, while Flax supports customization of existing models. It ends by asking about the future of domain-specific languages for AI. ### Source excerpt Google's AI division, Google Brain, has two main products for deep learning: TensorFlow and Jax. While TensorFlow is best known, Jax can be thought of as a higher-level language for specifying deep learning algorithms while automatically eliding code that doesn't need to run as part of the model. Jax evolved from Autograd, and is a combination of Autograd and XLA. Autograd "can automatically differentiate native Python and Numpy code. It can handle a large subset of Python's features, including loops, ifs, recursion and closures, and it can even take derivatives of derivatives of derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation), which means it can efficiently take gradients of scalar-valued functions with respect to array-valued arguments, as well as forward-mode differentiation, and the two can be composed arbitrarily. The main intended application of Autograd is gradient-based optimization." Flax is then built on top of Jax, and allows for easier customization of existing models. What do you see as the future of domain specific languages for AI? ## Using Python and OCaml in the same Jupyter notebook DevFeed: [Using Python and OCaml in the same Jupyter notebook]() Original publisher: [Read original article]() Author: Laurent Mazare Published: 2019-12-16T00:00:00Z Content type: tutorial Language: en Sources: [Jane Street]() Topics: [OCaml](), [Python](), [Jupyter Notebook](), [jupyter](), [Programming](), [NumPy]() Tags: [jupyter](), [jupyter-notebook](), [libraries](), [modules](), [ocaml](), [programming](), [programming-language](), [python]() ### AI overview A technical tutorial on running Python and OCaml code in the same Jupyter notebook. It explains how Jane Street uses pyml, OCaml bindings to the Python C API, plus a ppx extension and library to create bindings, exchange values, and reuse OCaml systems from Python workflows. ### Source excerpt The cover image is based on Jupiter family by NASA/JPL. ## PySurvival: An Open Source Python Package for Survival Analysis Modeling DevFeed: [PySurvival: An Open Source Python Package for Survival Analysis Modeling]() Original publisher: [Read original article]() Author: Stephane Fotso Published: 2019-04-12T21:06:21Z Content type: article Language: en Sources: [Square Corner Blog RSS Feed]() Topics: [Python](), [Open Source](), [NumPy](), [PyTorch](), [SciPy](), [Data analysis]() Tags: [analysis](), [data-analysis](), [data-science](), [machine-learning](), [open-source](), [python](), [pytorch](), [validation]() ### AI overview The article introduces PySurvival, an open source Python package for survival analysis modeling. It describes supported models, theory summaries, API examples, tutorials, performance metrics, and model loading and saving. ### Source excerpt PySurvival is an open source python package for Survival Analysis modeling. ## Introducing PySurvival DevFeed: [Introducing PySurvival]() Original publisher: [Read original article]() Author: sfotso Published: 2019-04-12T21:06:21Z Content type: article Language: en Sources: [Square Corner Blog - Medium]() Topics: [Python](), [Data analysis](), [NumPy](), [PyTorch](), [SciPy](), [Open Source]() Tags: [api](), [article](), [data-analysis](), [data-science](), [deep-learning](), [machine-learning](), [open-source](), [performance-metrics](), [python](), [pytorch](), [survival-analysis](), [tutorials]() ### AI overview Square introduces PySurvival, an open source Python package for survival analysis modeling. The article describes its models, theory summaries, API documentation, examples, tutorials, performance metrics, and model loading and saving features. ### Source excerpt Heads up, we've moved! If you'd like to continue keeping up with the latest technical content from Square please visit us at our new home https://developer.squareup.com/blogPySurvival is an open source python package for Survival Analysis modeling. Today, we're excited to introduce PySurvival, a python package for Survival Analysis modeling. This article is the first installment in a four part series, which will include tutorials designed to demonstrate how to easily make the most of the package. You can also find these tutorials on the official website: Churn modeling Predictive maintenance Credit risk PySurvival LogoWhat is PySurvival ? PySurvival is an open source python package for Survival Analysis modeling -- the modeling concept used to analyze or predict when an event is likely to happen. It is built on top the most commonly used machine learning packages: NumPy, SciPy, and PyTorch. PySurvival provides a very easy way to navigate between theoretical knowledge on Survival Analysis and detailed tutorials on how to conduct a full analysis, as well as build and use a model. The package contains: 10+ models ranging from the Cox Proportional Hazard model and the Neural Multi-Task Logistic Regression, to Random Survival Forest Summaries of the theory behind each model as well as API descriptions and examples Detailed tutorials on how to perform exploratory data analysis, survival modeling, cross-validation and prediction, for churn modeling and credit risk, for example Performance metrics to assess the models' abilities like c-index or brier score Simple ways to load and save models Installation If you have already installed a working version of gcc, the easiest way to install Pysurvival is using pip. pip install pysurvival The complete installation steps can be found here. Introduction to Survival analysisWhat is Survival Analysis ? Survival analysis is used to analyze or predict when an event is likely to happen. It originated in medical research, but its use has ## Neural Networks and Deep Learning DevFeed: [Neural Networks and Deep Learning]() Original publisher: [Read original article]() Published: 2017-11-09T00:00:00Z Content type: tutorial Language: en Sources: [Vasya Drobushkov]() Topics: [Deep neural networks](), [Neural Network](), [Deep learning](), [Mathematics](), [data](), [NumPy]() Tags: [arrays](), [cpu](), [deep-learning](), [learning](), [mathematics](), [matrix](), [neural-networks](), [training-data]() ### AI overview A concise synopsis of a Coursera course on neural networks and deep learning, covering supervised learning, training data, iterative prediction and correction, and vectorization. It emphasizes that array and matrix operations can be faster than explicit loops, while noting that the computational details are not fully understood by the author. ### Source excerpt Disclaimer It is brief synopsis of Neural Networks and Deep learning course on Coursera. That course is a first part of Deep learning specialization. Though course is great and very useful, I found that it has a lot of mathematics explanation (linear algebra -- matrices, derivatives etc.), so sometimes it was difficult to wait for the actual useful information I didn't know. So, I think I might be interested in reviewing neural networks in the future, but won't be able to review course information as there will be a lot of such noise. So, I decided to briefly put everything into one page, so I will be able to quickly review this topic in the future. There will be no code, just formulas as they were explained in a course with some notes on how I understood it. ## RealityMining, a Case Study in the Woes of Data Processing DevFeed: [RealityMining, a Case Study in the Woes of Data Processing]() Original publisher: [Read original article]() Published: 2014-01-21T22:11:49Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [data-processing](), [Python](), [dataset](), [SciPy](), [MATLAB](), [NumPy]() Tags: [array](), [big-data](), [data-analysis](), [data-mining](), [data-processing](), [dataset](), [how-to](), [library](), [matlab](), [python](), [reality-mining](), [research](), [social-networks](), [tutorial]() ### AI overview A tutorial and critique of accessing the RealityMining smartphone dataset with Python. It explains the dataset's communication, cell-tower, survey, and friendship data; the access process; the proprietary Matlab file format; and difficulties loading and inspecting the data with SciPy and NumPy. ### Source excerpt This post is intended to be a tutorial on how to access the RealityMining dataset using Python (because who likes Matlab?), and a rant on how annoying the process was to figure out. RealityMining is a dataset of smart-phone data logs from a group of about one hundred MIT students over the course of a year. The data includes communication and cell tower data, the latter being recorded every time a signal changes from one tower to the next. ## Principal Component Analysis DevFeed: [Principal Component Analysis]() Original publisher: [Read original article]() Published: 2012-06-28T12:08:44Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Data analysis](), [NumPy](), [Covariance](), [Python]() Tags: [covariance](), [data-analysis](), [eigenvalues](), [mathematics](), [principal-component-analysis](), [programming](), [python]() ### AI overview This tutorial explains principal component analysis as a way to reduce a dataset's dimensions by identifying directions of greatest variability. It outlines a Python and NumPy implementation that centers data, computes a covariance matrix, and obtains and sorts eigenvalues and principal components. ### Source excerpt Problem: Reduce the dimension of a data set, translating each data point into a representation that captures the "most important" features. Solution: in Python import numpy def principalComponents(matrix): # Columns of matrix correspond to data points, rows to dimensions. deviationMatrix = (matrix.T - numpy.mean(matrix, axis=1)).T covarianceMatrix = numpy.cov(deviationMatrix) eigenvalues, principalComponents = numpy.linalg.eig(covarianceMatrix) # sort the principal components in decreasing order of corresponding eigenvalue indexList = numpy.argsort(-eigenvalues) eigenvalues = eigenvalues[indexList] principalComponents = principalComponents[:, indexList] return eigenvalues, principalComponents Discussion: The problem of reducing the dimension of a dataset in a meaningful way shows up all over modern data analysis. ## Python in the Scientific World DevFeed: [Python in the Scientific World]() Original publisher: [Read original article]() Author: Guido van Rossum (noreply@blogger.com) Published: 2009-11-05T18:12:00Z Content type: article Language: en Sources: [Guido van Rossum]() Topics: [Python](), [Computing](), [NumPy](), [data](), [IPython]() Tags: [computing](), [data](), [library](), [python](), [real-time](), [time-series]() ### AI overview The article reports on a Py4Science meeting at UC Berkeley featuring lightning talks about projects using Python for scientific work. It describes Python and extensions such as NumPy being used across fields including neuroimaging, astronomy, genomics, and symbolic mathematics. ### Source excerpt Yesterday I attended a biweekly meeting of an informal a UC Berkeley group devoted to Python in science (Py4Science), organized by Fernando Perez. The format (in honor of my visit) was a series of 4-minute lightning talks about various projects using Python in the scientific world (at Berkeley and elsewhere) followed by an hourlong Q&A session. This meant I didn't have to do a presentation and still got to interact with the audience for an hour -- my ideal format. I was blown away by the wide variety of Python use for scientific work. It looks like Python (with extensions like numpy) is becoming a standard tool for many sciences that need to process large amounts of data, from neuroimaging to astronomy. Here is a list of the topics presented (though not in the order presented). All these describing Python software; I've added names and affiliations insofar I managed to get them. (Thanks to Jarrod Millman for providing me with a complete list.) Most projects are easily found by Googling for them, so I have not included hyperlinks except in some cases where the slides emphasized them. (See also the blog comments.) Fernando gave an overview of the core Python software used throughout scientific computing: NumPy, Matplotlib, IPython (by Fernando), Mayavi, Sympy (about which more later), Cython, and lots more. On behalf of Andrew Straw (Caltech), Fernando showed a video of an experimental setup where a firefly is tracked in real time by 8 camaras spewing 100 images per second, using Python software. Nitimes, a time-series analysis tool for neuroimaging, by Ariel Rokern (UCB). A comparative genomics tool by Brent Pedersen of the Freeling Lab / Plant Biology (UCB). Copperhead: Data-Parallel Python, by Bryan Catanzaro (working with Armando Fox) and others. Nipype: Neuroimaging analysis pipeline and interfaces in Python, by Chris Burns (http://nipy.sourceforge.net/nipype/). SymPy -- a library for symbolic mathematics in Pure Python, by Ondrej Certik (runs on Google App Engin ## Choosing a message queue for Python on Ubuntu on a VPS DevFeed: [Choosing a message queue for Python on Ubuntu on a VPS]() Original publisher: [Read original article]() Author: Graham King Published: 2009-08-10T05:05:13Z Content type: comparison Language: en Sources: [Graham King]() Topics: [Messaging](), [RabbitMQ](), [NumPy](), [Ubuntu](), [Redis]() Tags: [comparison](), [gearman](), [message-queue](), [python](), [queue](), [rabbitmq](), [redis](), [software](), [ubuntu]() ### AI overview A comparison of RabbitMQ, Gearman, Beanstalkd, and Redis as message queues for Python web applications running on Ubuntu VPS environments. It considers Python compatibility, memory usage, reliability, and suitability for background work. ### Source excerpt Queuing up the best message options: A straightforward comparison of popular message queues.