Planet SciPy
Debunking the Biggest Myths in Data Science
As data scientists continually seek to integrate more effectively with other business units in their organizations, it’s essential to take the time to dispel common myths like these, where feasible. Raising awareness for how data scientists work can help improve everything from the accuracy of model predictions to the quality of candidates recruited to fill open positions.Amazon Review Summarization Using GPT-2 And PyTorch
Since its reveal in 2017 in the popular paper Attention Is All You Need (https://arxiv.org/abs/1706.03762), the Transformer quickly became the most popular model in NLP. The ability to process text in a non-sequential way (as opposed to RNNs) allowed for training of big models. The attention mechanism it introduced proved extremely useful in generalizing text.
Following the paper, several popular transformers surfaced, the most popular of which is GPT. GPT models are developed and trained by OpenAI, one of the leaders in AI research. The latest release of GPT is GPT-3, which has 175 billion parameters. The model was very advanced to the point where OpenAI chose not to open-source it. People can access it through an API after a signup process and a long queue.
However, GPT-2, their previous release is open-source and available on many deep learning frameworks.
In this excercise, we use Huggingface and PyTorch to fine-tune a
Pix2pix: Key Model Architecture Decisions
Generative Adversarial Networks or GANs is a type of neural network that belongs to the class of unsupervised learning. It is used for the task of deep generative modeling. In deep generative modeling, the deep neural networks learn a probability distribution over a given set of data points and generate similar data points. Since it […]
The post Pix2pix: Key Model Architecture Decisions appeared first on neptune.ai.
XGBoost vs LightGBM: How Are They Different
Gradient Boosted Machines and their variants offered by multiple communities have gained a lot of traction in recent years. This has been primarily due to the improvement in performance offered by decision trees as compared to other machine learning algorithms both in products and machine learning competitions. Two of the most popular algorithms that are […]
The post XGBoost vs LightGBM: How Are They Different appeared first on neptune.ai.
The Best Weights & Biases Alternatives
Weights & Biases, also known as WandB, is an MLOps tool for performance visualization and experimental tracking of machine learning models. It helps with automation, tracking, training, and improvement of ML models. Weights & Biases is a cloud-based service that allows you to host your experiments in a single central repository and if you have […]
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Hugging Face Pre-trained Models: Find the Best One for Your Task
When you are working on a Machine learning problem, adapting an existing solution and repurposing it can help you get to a solution much faster. Using existing models, not just aid machine learning engineers or data scientists but also helps companies to save computational costs as it requires less training. There are many companies that […]
The post Hugging Face Pre-trained Models: Find the Best One for Your Task appeared first on neptune.ai.
Best Tools for ML Model Governance, Provenance, and Lineage
ML software development is complex; building an ML model is one thing, improving and maintaining it, is another. If you want your machine learning models to be robust, compliant, and give reproducible results, you must invest time and money in quality model management. Model governance, model provenance, and model lineage tools help you in doing […]
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How Heavily-Regulated Industries Can Accelerate Open-Source Innovation
IBM® Anaconda Repository for IBM Cloud Pak® for Data can be installed in air-gapped environments to provide organizations access to curated, open-source packages without connecting to the internet. Anaconda Repository allows enterprises to centralize their data science projects and confidently manage the security of their open-source packages and libraries used for AI.Anaconda Individual Edition 2021.11
Beyond this Anaconda Individual Edition release, we’d like to mention that there is an initial macOS Apple M1 Miniconda installer for Python 3.8 available in the Miniconda Repository. The Miniconda installer and other available packages are built and tested on Apple M1 machines. More information on Miniconda with the latest installer links can be found in the Miniconda - Conda documentation. Although there is not an Anaconda Individual Edition 2021.11 installer for macOS Apple M1, there is a comprehensive list of packages available to be installed for macOS Apple M1 with Conda available here. We will share more information about this when it is available.Moving From TensorFlow To PyTorch
The concept of Deep Learning frameworks, libraries, and numerous tools exist to reduce the large amounts of manual computations that must otherwise be calculated. TensorFlow and PyTorch are currently two of the most popular frameworks to construct neural network architectures. While TensorFlow was released a year before PyTorch, most developers are tending to shift towards […]
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A vision for extensibility to GPU & distributed support for SciPy, scikit-learn, scikit-image and beyond
Over the years, array computing in Python has evolved to support distributed arrays, GPU arrays, and other various kinds of arrays that work with specialized hardware, or carry additional metadata, or use different internal memory representations. The foundational library for array computing in the PyData ecosystem is NumPy. But NumPy alone is a CPU-only library - and a single-threaded one at that - and in a world where it's possible to get a GPU or a CPU with a large core count in the cloud cheaply or even for free in a matter of seconds, that may not seem enough. For the past couple of years, a lot of thought and effort has been spent on devising mechanisms to tackle this problem, and evolve the ecosystem in a gradual way towards a state where PyData libraries can run on a GPU, as well as in distributed mode across multiple GPUs.
We feel like a shared vision has emerged, in bits and pieces. In this post, we aim to articulate that vision and
How Data Visualization Improves Decision-Making
There are many good tools for data visualization, ranging from the built-in graphing of Microsoft Excel to the business-intelligence plotting and dashboarding tools like Tableau, Looker, and Microsoft’s Power BI. What about when you want to integrate plotting with your Python data analytics workflows? Luckily, there are many solid Python plotting options as well, all of which are listed and compared at pyviz.org. Matplotlib, Plotly, and Bokeh (along with tools built on top of them) are the most popular and are all solid choices. Any of these tools can help you make sure you draw the right conclusions at every step of your analysis and can help you build presentations driven directly from Python.A bioinformatics training career panel in the DIB Lab
Careers in training!
Keeping your Conda Environments Safe and Secure with your Anaconda Nucleus Account
Our goal was to provide a small but significant step towards linking your local workspace to your Nucleus account, but we’re not done. Near-term, we’ve planned several changes to environment sync to make it easier to use, more flexible, and more capable. Many of those changes are coming in the next few months.K-Means Clustering Explained
Clustering was introduced in 1932 by H.E. Driver and A.L.Kroeber in their paper on “Quantitative expression of cultural relationship”. Since then this technique has taken a big leap and has been used to discover the unknown in a number of application areas eg. Healthcare. Clustering is a type of unsupervised learning where the references need […]
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NumPy Benchmarking
In this blog post, I'll be talking about my journey in Quansight. I want to share all things I was involved in and accomplished. What issues I faced, and most importantly, what were awesome life hacks I learned during this period.
First of all, I'd like to express my gratitude to the whole team for allowing me to be a part of such a great team. My work was majorly focused on providing performance benchmarks to NumPy in realistic situations. The target was to show the world that NumPy is efficient in handling quasi real-life situations too.
The primary technical outcome of my work is available in the numpy documentation.
Read more… (6 min remaining to read)
Top Model Versioning Tools for Your ML Workflow
In recent times, Machine Learning has gained importance due to its ability to guide businesses in making precise and accurate decisions. Under the hood, Machine Learning is an iterative and repetitive process. Series of training jobs are done to optimize a model’s predictive performance. Without the right methods, it is easy to lose track of […]
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Hiring an engineer and post-doc to simplify data science on dirty data
Note
Join us to work on reinventing data-science practices and tools to produce robust analysis with less data curation.
It is well known that data cleaning and preparation are a heavy burden to the data scientist.
In the dirty data project, we have been conducting machine-learning research …
Arize AI & Neptune AI Partnership: Continuous Monitoring, Continuous Improvements for ML Models
Delivering the best machine learning model to production should be as easy as training, testing, and deploying — right? Not quite! Models are far from perfect as they move from research to production, and maintaining model performance once in production is even more challenging. Once out of the offline research environment, the data a model consumes […]
The post Arize AI & Neptune AI Partnership: Continuous Monitoring, Continuous Improvements for ML Models appeared first on neptune.ai.
Scale your pandas workflow with Modin – no rewrite required
Notices and DisclaimersDimensionality Reduction for Machine Learning
Data forms the foundation of any machine learning algorithm, without it, Data Science can not happen. Sometimes, it can contain a huge number of features, some of which are not even required. Such redundant information makes modeling complicated. Furthermore, interpreting and understanding the data by visualization gets difficult because of the high dimensionality. This is […]
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TorchVision Datasets: Getting Started
The TorchVision datasets subpackage is a convenient utility for accessing well-known public image and video datasets. You can use these tools to start training new computer vision models very quickly. TorchVision Datasets Example To get started, all you have to do is import one of the Dataset classes. Then, instantiate ... Read More
The post TorchVision Datasets: Getting Started appeared first on Sparrow Computing.
NumPy Any: Understanding np.any()
The np.any() function tests whether any element in a NumPy array evaluates to true: The input can have any shape and the data type does not have to be boolean (as long as it’s truthy). If none of the elements evaluate to true, the function returns false: Passing in a ... Read More
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Dataframe interchange protocol: cuDF implementation
This is Ismaël Koné from Côte d'Ivoire (Ivory Coast). I am a fan of open source software.
In the next lines, I'll try to capture my experience at Quansight Labs as an intern working on the cuDF implementation of the dataframe interchange protocol.
We'll continue by motivating this project through details about cuDF and the dataframe interchange protocol.
Read more… (9 min remaining to read)
An efficient method of calling C++ functions from numba using clang++/ctypes/rbc
The aim of this post is to explore a method of calling C++ library
functions from Numba compiled
functions
--- Python functions that are decorated with
numba.jit(nopython=True).
While there exist ways to wrap C++ codes to Python (see Appendix below), calling these wrappers from Numba compiled functions is often not as straightforward and efficient as one would hope.
Read more… (5 min remaining to read)
Why Data Visualization is One of the Hardest but Most Important Tasks
As data scientists, we have the power to help shape business decisions, public policy, medical research, and other essential areas of daily life. It’s incumbent on us to practice our craft responsibly and ethically, and that includes the data visualization process. To the best of our ability, we need to ensure our visualizations make clear any assumptions or biases that might be baked into our results, and that they support viewers in asking further questions, rather than serving as a “period” on any discussion. Whether exploratory or narrative in purpose, data visualizations will fundamentally anchor the way the data and topic are viewed, so if it’s worth making a chart in the first place, it’s worth taking the time to do it right.Array Libraries Interoperability
In this blog post I talk about the work that I was able to accomplish during my internship at Quansight Labs and the efforts being made towards making array libraries more interoperable.
Going ahead, I'll assume basic understanding of array and tensor libraries with their usage in the Python Scientific and Data Science software stack.
Master NumPy leading the young Tensor Turtles
Read more… (15 min remaining to read)
Re-Engineering CI/CD pipelines for SciPy
In this blog post I talk about the projects and my work during my internship at Quansight Labs. My efforts were geared towards re-engineering CI/CD pipelines for SciPy to make them more efficient to use with GitHub Actions. I also talk about the milestones that I achieved, along with the associated learnings and improvements that I made.
This blog post would assume a basic understanding of CI/CD and GitHub Actions. I will also assume a basic understanding of Python and the SciPy ecosystem.
Re-Engineering CI/CD pipelines for SciPy
Read more… (14 min remaining to read)
PyTorch DataLoader Quick Start
PyTorch comes with powerful data loading capabilities out of the box. But with great power comes great responsibility and that makes data loading in PyTorch a fairly advanced topic. One of the best ways to learn advanced topics is to start with the happy path. Then add complexity when you ... Read More
The post PyTorch DataLoader Quick Start appeared first on Sparrow Computing.
Celebrating Hispanic Heritage Month: Hispanic and Latino(a)(x) Innovators Who Inspire Us at Anaconda
National Hispanic Heritage Month, celebrated from September 15th to October 15th, recognizes the histories, cultures, and contributions of Hispanic and Latinx Americans. The term Hispanic refers to someone from, or is a descendant of, a Spanish-speaking country. Latino(a)(x) refers to someone who comes from Latin America, or is a descendant from any Latin American country. While it’s called “Hispanic Heritage Month,” this month is meant to recognize a broader group of Americans whose ancestors came from Spain, Mexico, the Caribbean, Central, and South America.How the NumPy append operation works
Understanding the np.append() operation and when you might want to use it.
The post How the NumPy append operation works appeared first on Sparrow Computing.
Using Hypothesis to test array-consuming libraries
Over the summer,
I've been interning at Quansight Labs
to develop testing tools
for the developers and users
of the upcoming Array API standard.
Specifically,
I contributed "strategies"
to the testing library Hypothesis,
which I'm excited to announce
are now available in hypothesis.extra.array_api.
Check out the primary pull request I made
for more background.
This blog post is for anyone developing array-consuming methods (think SciPy and scikit-learn) and is new to property-based testing. I demonstrate a typical workflow of testing with Hypothesis whilst writing an array-consuming function that works for all libraries adopting the Array API, catching bugs before your users do.
Read more… (12 min remaining to read)
Webinar Q&A: Accelerating Product Reformulation with Machine Learning
In our recent C&EN Webinar: Accelerating Consumer Products Reformulation with Machine Learning, we demonstrated how to leverage digital tools and technology to bring new products to market faster. The webinar was well attended by scientists, engineers, and business leaders across the product development spectrum eager to learn how these concepts can be applied to their …Continue Reading
Dataframe interchange protocol and Vaex
The work I briefly describe in this blog post is the implementation of the dataframe interchange protocol into Vaex which I was working on through the three month period as a Quansight Labs Intern.
Connection between dataframe libraries with dataframe protocol
Today there are quite a number of different dataframe libraries available in Python. Also, there are quite a number of, for example, plotting libraries. In most cases they accept only the general Pandas dataframe and so the user is quite often made to convert between dataframes in order to be able to use the functionalities of a specific plotting library. It would be extremely cool to be able to use plotting libraries on any kind of dataframe, would it not?
Read more… (13 min remaining to read)
Low-code contributions through GitHub
Healthy, inclusive communities are critical to impactful open source projects. A challenge for established projects is that the history and implicit technical debt increase the barrier to contribute to significant portions of code base. The literacy of large code bases happens over time through incremental contributions, and we'll discuss a format that can help people begin this journey.
At Quansight Labs, we are motivated to provide opportunities for new contributors to experience open source community work regardless of their software literacy. Community workshops are a common format for onboarding, but sometimes the outcome can be less than satisfactory for participants and organizers. In these workshops, there are implicit challenges that need to be overcome to contribute to projects' revision history like Git or setting up development environments.
Our goal with the following low-code workshop is to offer a way for folks to join a project's contributors list without the technical overhead. To achieve this we'll discuss a format that relies solely on the GitHub web interface.
Read more… (5 min remaining to
Securing the Open-Source Pipeline with Anaconda CVE Curation
Take advantage of Anaconda Team Edition to secure your open-source pipeline so your team can spend more time building models, analyzing data, and making data-driven decisions.What’s new with fastparquet?
In addition to what is detailed above, other external changes were happening in the background. While some required fixes, others offered opportunities to continue improving the performance and features of fastparquet. I'm excited about the advancements we've made today and look forward to sharing a future update about the continued progress with this project.Not a checklist: different accessibility needs in JupyterLab
In a pandemic, the template joke-starter “x and y walk into a bar” seems like a stretch from my reality. So let’s try this remote version:
Two community members with accessibility knowledge enter a virtual meeting room to talk about JupyterLab. They’ve both updated themselves on GitHub issues ahead of time. They’ve both identified major problems with the interface. They both get ready to express to the rest of the community what is indisputably, one hundred percent for-sure the biggest accessibility blocker in JupyterLab for users. Here it is, the moment of truth!
And they each say totally different things.
Read more… (5 min remaining to read)
Hiring someone to develop scikit-learn community and industry partners
Note
With the growth of scikit-learn and the wider PyData ecosystem, we want to recruit in the Inria scikit-learn team for a new role. Departing from our usual focus on excellence in algorithms, statistics, or code, we want to add to the team someone with some technical understanding, but an …
Introducing Enthought Edge: Unlocking the Value of R&D Data
While the value of R&D data is clear, finding a way to sort through it can be daunting given the special handling required to extract its value. In fact, 75 percent of surveyed R&D executives believe advanced analytics techniques would play a pivotal role in their future R&D activities, but only 25 percent state that …Continue Reading
Introducing Enthought Edge
Introducing Enthought Edge: A New DataOps Solution Designed to Unlock the Value in R&D Data Designed for scientists, by scientists, Edge centralizes and standardizes data in easily accessible, analysis-ready form. Early Access Program now available. Austin, TX – September 1, 2021 – Enthought, the leading provider of services and technology powering digital transformation for science, …Continue Reading
Using snakemake to do simple wildcard operations on many, many, many files
snakemake is awesome
A paper on the Lees-Edwards method
A few years ago1, Sebastian contacted me to help with simulations. Great, I like simulation studies, so we start discussing the details. The idea: use an established method, the Lees-Edwards boundary condition, to study colloids under shear.
Machine Learning in Materials Science
The process of materials discovery is complex and iterative, requiring a level of expertise to be done effectively. Materials workflows that require human judgement present a specific challenge to the discovery process, which can be leveraged as an opportunity to introduce digital technologies. In the lab, many tasks require manual data collection and judgement. And …Continue Reading
FORGE-ing Ahead: Charting the Future of Geothermal Energy
A microseismic event loaded from the Frontier Observatory for Research in Geothermal Energy (FORGE) distributed acoustic sensing (DAS) data into a Jupyter notebook showing energy from a microseismic event arriving at about 7.5 seconds. These microseisms bring information about the process of stimulation. However, in the data set there are relatively few and they are …Continue Reading
A biotech career panel in the DIB Lab
Careers outside of universities!
Scaling sourmash to millions of samples
Bigger and better!
Poetry for Package Management in Machine Learning Projects
When you’re building a production machine learning system, reproducibility is a proxy for the effectiveness of your development process. But without locking all your Python dependencies, your builds are not actually repeatable. If you work in a Python project without locking long enough, you will eventually get a broken build ... Read More
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Development containers in VS Code: a quick start guide
If you’re building production ML systems, dev containers are the killer feature of VS Code. Dev containers give you full VS Code functionality inside a Docker container. This lets you unify your dev and production environments if production is a Docker container. But even if you’re not targeting a Docker ... Read More
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New sourmash databases are available!
Databases are now available for GTDB!
Colunando no O Estado do Piauí
O Estado do Piauí é um novo jornal que surgiu recentemente pelas bandas de lá. Com um foco maior em reportagens longas e densas, misturando jornalismo investigativo e literário, o projeto pretende discutir em profundidade os temas de interesse do estado, descobrir histórias piauienses únicas, repercutir situações problemáticas, apontar alternativas e muito mais. Não se… Continue a ler »Colunando no O Estado do PiauíLessons for Geoscientists from the book Real World AI: A Practical Guide for Responsible Machine Learning
In this blog article Enthought Energy Solutions vice president Mason Dykstra looks at the recently published book titled “Real World AI: A Practical Guide for Responsible Machine Learning” in the context of both the technical challenges faced by geoscientists and how to scale. Author: Mason Dykstra, Ph.D., Vice President, Energy Solutions In the newly released …Continue Reading
Leveraging AI in Cell Culture Analysis
Mammalian cell culture is a fundamental tool for many discoveries, innovations and products in the life sciences. Currently, cells are the smallest unit of sustainable life outside the body, thereby providing an essential platform for testing hypotheses and mimicking biological processes. The applications of cell culture, while not limitless, are plentiful. Every cell type, downstream …Continue Reading
Ciclo de Entrevistas sobre as Pesquisas no PPGCC da UFPA – Inteligência Computacional
A Faculdade de Computação e o Programa de Pós-Graduação em Ciência da Computação da UFPA estão desenvolvendo um projeto que pretende atingir dois objetivos: o primeiro, fazer uma melhor divulgação para o público externo à universidade do que produzimos em nossas pesquisas; o segundo, uma melhor divulgação INTERNA da mesma coisa – o que desenvolvemos… Continue a ler »Ciclo de Entrevistas sobre as Pesquisas no PPGCC da UFPA – Inteligência ComputacionalEnthought Announces Formation of Digital Transformation, Materials Science Advisory Boards
Austin, TX – June 15, 2021 – Enthought, the leading provider of technologies and services that deliver digital innovation to science-driven companies, is experiencing rapid growth as companies look to accelerate their adoption of new technologies, such as artificial intelligence and machine learning, in response to COVID-19. In support of Enthought’s growth, strategic vision and …Continue Reading
Moving sourmash towards more community engagement - a funding application
CZI EOSS4 application for sourmash support
Visualization with Seaborn
This article will enable you to use the seaborn python package to visualize your structured data with seaborn barchart, scatter plot, seaborn histogram, line, and seaborn distplot.Searching all public metagenomes with sourmash
Searching all the things!
Introduction of Fast Fourier Transformation (FFT)
This article comprises of introduction to the Fourier series, Fourier analysis, Fourier transformation, why do we use it, an explanation of the FFT algorithm, and its implementation.Understanding of Probability Distribution and Normal Distribution
Introduction of probability distribution and its types. Here you can find the intuition about the normal or gaussian distribution, standard normal distribution with the normal curve and normal distribution formula.Is your software ready for the Journal of Open Source Software?
For the unaware reader, the Journal of Open Source Software (JOSS) is an open-access scientific journal founded in 2016 and aimed at publishing scientific software. A JOSS article in itself is short and its publication contributes to recognize the work on the software. I share here my point of view on what makes some software tools more ready to be published in JOSS. I do not comment on the size or the relevance for research which are both documented on JOSS' website.
Using Autoencoder to generate digits with Keras
This article contains a real-time implementation of an autoencoder which we will train and evaluate using very known public benchmark dataset called MNIST data.Understanding of Support Vector Machine (SVM)
Explanation of the support vector machine algorithm, the types, how it works, and its implementation using the python programming language with the sklearn machine learning packageBasic Counting in Python
I love fancy machine learning algorithms as much as anyone. But sometimes, you just need to count things. And Python’s built-in data structures make this really easy. Let’s say we have a list of strings: With a list like this, you might care about a few different counts. What’s the ... Read More
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Confidence Interval Understanding
Explanation of confidence intervals and the how-to calculate it for different scenarios, and also the equation that makes the confidence interval and the parameters involved with itDecision Trees
Intuition and implementation of the first tree-based algorithm in machine learningDimensionality Reduction, PCA Intro
We will be covering a dimensionality reduction algorithm called PCA (Principal Components Analysis) and will show how it helps to understand the data you have.Understanding Autoencoders - An Unsupervised Learning approach
This article covers the concept of Autoencoders. Concepts like What are Autoencoders, Architecture of an Autoencoder, and intuition behind the training of Autoencoders.How to Use the PyTorch Sigmoid Operation
The PyTorch sigmoid function is an element-wise operation that squishes any real number into a range between 0 and 1. This is a very common activation function to use as the last layer of binary classifiers (including logistic regression) because it lets you treat model predictions like probabilities that their ... Read More
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Optimization Methods, Gradient Descent
This article covers a sublime explanation and a simple example of Vanilla Gradient Descent algorithm, Stochastic Gradient Descent, Momentum Optimizer, and Adam Optimizer in which RMSProp is also explainedUnderstanding of Regularization in Neural Networks
This article includes the different techniques of regularization like Data Augmentation, L1, L2, Dropout, and Early StoppingDiving into Object Detection Basics
A guide for Object Detection basic concepts which cover What is Object Detection and how does it work, Concept of Anchor Boxes, Why is Loss function necessary, some free datasets, and finally, implementation of SSD.Normalization in Deep learning
Different types of Normalization in Deep Learning. A very useful technique to avoid overfitting and generalize your model better.Dropout in Deep Learning
Understanding Dropouts in Deep Learning to reduce overfittingYolov3 and Yolov4 in Object Detection
Explanation of object detection with various use cases and algorithms. Specifically, how the yolov3 and yolov4 architectures are structured, and how they perform object detectionEnd-To-End PyTorch Example of Image Classification with Convolutional Neural Networks
Image classification solutions in PyTorch with popular models like ResNet and its variations. End-To-End solution for CIFAR10/100 and ImageNet datasets.Supervised learning with Scikit-Learn Library
How to create a model for supervised learning like linear and logistic regression with scikit-learn python libraryLinear and Logistic Regression
Intuition and implementation behind the base algorithms for supervised machine learningRandom Forests Understanding
Intuition and Implementation on a key algorithm to reduce overfitting in tree based algorithmsActivation Functions for Neural Networks
In this article, explaination of various activation functions has been given like Linear, ELU, ReLU, Sigmoid, and tanh.AI Needs the ‘Applied Sciences’ Treatment
As industries rapidly advance in AI/machine learning, a key to unlocking the power of these approaches for companies is an enabling environment. Domain experts need to be able to use artificial intelligence on data relevant to their work, but they should not have to know computer or data science techniques to solve their problems. An …Continue Reading
On the Link Between Optimization and Polynomials, Part 4
While the most common accelerated methods like Polyak and Nesterov incorporate a momentum term, a little known fact is that simple gradient descent –no momentum– can achieve the same rate through only a well-chosen sequence of step-sizes. In this post we'll derive this method and through simulations discuss its practical …
NumFOCUS Welcomes Tesco Technology to Corporate Sponsors
NumFOCUS is pleased to announce our new partnership with Tesco Technology. A long-time PyData event sponsor, Tesco Technology joined NumFOCUS as a Silver Corporate Sponsor in December 2020. “We are very excited to formalize our partnership with Tesco Technology,” said Leah Silen, NumFOCUS Executive Director. “Tesco Technology has partnered with NumFOCUS for the past several […]
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Job Posting | Communications and Marketing Manager
Job Title: Communications and Marketing Manager Position Overview The primary role of the Communications & Marketing Manager is to manage the NumFOCUS brand by overseeing all outgoing communications between NumFOCUS and our stakeholders. You will serve the project communities by playing a key role in their event marketing management and assist with project promotional and […]
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Getting started with Acoular - Part 1
This is the first in a series of three blog posts about the basic use of Acoular. It explains some fundamental concepts and walks through a simple example. Acoular is a Python library that processes multichannel data (up to a few hundred channels) from acoustic measurements with a microphone array. The focus of the processing is on the construction of a map of acoustic sources. This is somewhat similar to taking an acoustic photograph of some sound sources.Getting started with Acoular - Part 2
This is the second in a series of three blog posts about the basic use of Acoular. It assumes that you already have read the first post and continues by explaining some more concepts and additional methods. Acoular is a Python library that processes multichannel data (up to a few hundred channels) from acoustic measurements with a microphone array. The focus of the processing is on the construction of a map of acoustic sources. This is somewhat similar to taking an acoustic photograph of some sound sources.Getting started with Acoular - Part 3
This is the third and final in a series of three blog posts about the basic use of Acoular. It assumes that you already have read the first two posts and continues by explaining additional concepts to be used with time domain methods. Acoular is a Python library that processes multichannel data (up to a few hundred channels) from acoustic measurements with a microphone array. The focus of the processing is on the construction of a map of acoustic sources. This is somewhat similar to taking an acoustic photograph of some sound sources. To continue, we do the same set up as in Part 1. However, as we are setting out to do some signal processing in time domain, we define only TimeSamples, MicGeom, RectGrid and SteeringVector objects but no PowerSpectra or BeamformerBase. import acoular ts = acoular.TimeSamples( name="three_sources.h5" ) mg = acoular.MicGeom( from_file="array_64.xml" ) rg = acoular.RectGrid( x_min=-0.2, x_max=0.2, y_min=-0.2, y_max=0.2, z=0.3, increment=0.01 ) st = acoular.SteeringVector( grid=rg, mics=mg (continued...)Geophysics in the Cloud Competition
Join the 2021 GSH Geophysics in the cloud competition. Build a novel seismic inversion app and access all the data on demand with serverless cloud storage. Example notebooks show how to access this data and use AWS SageMaker to build your ML models. With prizes. Author: Ben Lasscock, Ph.D., Manager, Strategic Technologies, Energy Solutions …Continue Reading
PyTorch Tensor to NumPy Array and Back
You can easily convert a NumPy array to a PyTorch tensor and a PyTorch tensor to a NumPy array. This post explains how it works.
The post PyTorch Tensor to NumPy Array and Back appeared first on Sparrow Computing.
TorchVision Transforms: Image Preprocessing in PyTorch
TorchVision, a PyTorch computer vision package, has a great API for image pre-processing in its torchvision.transforms module. This post gives some basic usage examples, describes the API and shows you how to create and use custom image transforms.
The post TorchVision Transforms: Image Preprocessing in PyTorch appeared first on Sparrow Computing.
sourmash 4.0 is now available! Low low cost if you buy now!
sourmash v4.0.0 is here!
On the Link Between Optimization and Polynomials, Part 3
I've seen things you people wouldn't believe.
Valleys sculpted by trigonometric functions.
Rates on fire off the shoulder of divergence.
Beams glitter in the dark near the Polyak gate.
All those landscapes will be lost in time, like tears in rain.
Time to halt.
A momentum optimizer *
Introducing PyMC Labs: Saving the World with Bayesian Modeling
After I left Quantopian in 2020, something interesting happened: various companies contacted me inquiring about consulting to help them with their PyMC3 models.
Usually, I don't hear how people are using PyMC3 -- they mostly show up on GitHub or Discourse when something isn't working right. So, hearing about all these …
Using MicroPython and uploading libraries on Raspberry Pi Pico — Using rshell to upload custom code
MicroPython is an implementation of the Python 3 programming language, optimized to run microcontrollers. It's one of the options available for programming your Raspberry Pi Pico and a nice friendly way to get started with microcontrollers.
MicroPython can be installed easily on your Pico, by following the instructions on the …
sourmash v4.0.0 release candidate 1 is now available for comment!
sourmash v4.0.0 is coming!
Job Posting | Events and Digital Marketing Coordinator
Job Title: Events and Digital Marketing Coordinator Position Overview The primary role of the Events and Digital Marketing Coordinator is to support and assist the Events Manager and the Community Communications and Marketing Manager to advance one of NumFOCUS’s primary missions of educating and building the community of users and developers of open source scientific […]
The post Job Posting | Events and Digital Marketing Coordinator appeared first on NumFOCUS.
Transition your Python project to use pyproject.toml and setup.cfg! (An example.)
Updating old Python packages, in this year of the PSF 2021!
SAM Coupé SCREEN$ Converter — Interrupt optimizing image converter
The SAM Coupé was a British 8 bit home computer that was pitched as a successor to the ZX Spectrum, featuring improved graphics and sound and higher processor speed.
The SAM Coupé's high-color MODE4 could manage 256x192 resolution graphics, with 16 colors from a choice of 128. Each pixel can …
A snakemake hack for checkpoints
snakemake checkpoints r awesome