SciPy

Planet SciPy

neptune.ai 2021-09-16 10:52:40

MLOps Model Stores: Definition, Functionality, Tools Review

What is ML (Machine Learning) Model Store? Here’s a scenario you might find yourself in. You’ve gone through a rigorous development workflow, experimenting and training various machine learning models with different results and performance scores. You decide the best way to collaborate is by sharing your models stored in object storage like S3 or GCS […]

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neptune.ai 2021-09-15 16:30:26

Visualizing Machine Learning Models: Guide and Tools

Why do we need to visualize Machine Learning models? “If you refuse to trust decision-making to something whose process you don’t entirely understand, then why even hire people to work? No one knows how the human brain (with its hundred billion neurons!) makes decisions.” – Cassie Kozyrkov This quote has been used by some people […]

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Share Your R and Python Notebooks 2021-09-15 09:05:45.210770

PyTorch Tutorial A Complete Use Case Example

PyTorch Tutorial: A Complete Use-case Example
Introduction

This tutorial shows a full use-case of PyTorch in order to explain several concepts by example. The application will be hand-written number detection using MNIST. MNIST is a popular (perhaps the most popular) educational computer vision dataset. It is composed of 70K images of hand-written digits (0-9) split into 60K-10K training and test sets respectively. The images are tiny (28x28), which makes them easy to work with.

Contents:
  1. Data loading
    • Loading for tables
    • Loading for text (NLP)
    • Loading for images (CV)
  2. Neural Network building
    • Skeleton
    • Layers
    • Activation functions
  3. ML components
    • Loss functions
    • Optimizer
  4. Training loop
  5. Testing
  6. Saving/loading models
PyTorch Data Loading

When using PyTorch, there are many ways to load your data. It depends mainly on the type of data (tables, images, text, audio, etc.) and the size. Many text datasets are small enough to load into memory in full. Some image datasets (such as MNIST can also be loaded to memory in full due to the small image size. However, in most real-life applications,

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Quansight Labs 2021-09-14 18:00:00

Not a checklist: different accessibility needs in JupyterLab

JupyterLab Accessibility Journey Part 3

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.

What? Is that not a very funny joke? You’re right, it’s not funny at all; this is a real problem we faced.

When I say it like this, I feel silly that there was ever expectation of perfect agreement. And yet with every accessibility quick guide, code snippet, and blog post (like this one!), I feel as though

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Gaël Varoquaux - programming 2021-09-13 22:00:00

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 …

neptune.ai 2021-09-13 06:02:54

Version Control for ML Models: Why You Need It, What It Is, How To Implement It

Version control is important in any software development environment, and even more so in machine learning. In ML, the development process is very complex. It includes huge amounts of data, testing of multiple models, optimization of parameters, tuning of features, and more. If you want your research to be reproducible, you need proper version control […]

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Anaconda Blog 2021-09-09 14:00:00

Why Anaconda Created a Company Policy to Give More Time Off

As we continue to develop as a company and see changes in how we work, live, and spend our free time, we will continue to grow our policies to best match the needs of our team. We’re excited to see how our company culture continues to evolve and the impact of Snake Days on our team’s morale and wellbeing over time.
neptune.ai 2021-09-09 06:12:39

How to Compare Machine Learning Models and Algorithms

Machine learning has expanded rapidly in the last few years. Instead of simple, one-directional or linear ML pipelines, today data scientists and developers run multiple parallel experiments that can get overwhelming even for large teams. Each experiment is expected to be recorded in an immutable and reproducible format, which results in endless logs with invaluable […]

The post How to Compare Machine Learning Models and Algorithms appeared first on neptune.ai.

neptune.ai 2021-09-08 06:10:37

Should You Use Jupyter Notebooks in Production?

In the past couple of years, Notebooks have become a popular tool in fields like data science and machine learning, scientific research, genomics, and more. Jupyter Notebooks have been around for quite some time now. They’re used a lot in machine learning, mainly for experimentation and visualization. However, recently notebooks have been making progress into […]

The post Should You Use Jupyter Notebooks in Production? appeared first on neptune.ai.

neptune.ai 2021-09-07 06:00:37

DVC Alternatives For Experiment Tracking

Experiment Tracking is a technique for linking variables to the changes that those variables cause in your data. You can test many different combinations of variables—run multiple experiments with weights assigned to each one and see which are the most effective when aggregated together.  One of the challenges with experiment tracking is choosing the right […]

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Quansight Labs 2021-09-06 17:39:34

Making Numpy Accessible: Guidelines and Tools

Header illustration by author, Mars Lee

Numpy is now foundational to Python scientific computing. Our efforts reach millions of developers each month. As our user base grows, we recognize that we are neglecting the disabled community by not having our website and documentation up to modern accessibility standards.

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neptune.ai 2021-09-03 11:00:02

Object Detection with YOLO: Hands-on Tutorial

Object Detection as a task in Computer Vision We encounter objects every day in our life. Look around, and you’ll find multiple objects surrounding you. As a human being you can easily detect and identify each object that you see. It’s natural and doesn’t take much effort.  For computers, however, detecting objects is a task […]

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Blog – Enthought 2021-09-01 21:44:06

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, …
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Quansight Labs 2021-08-31 17:01:00

CZI EOSS4 Grants at Quansight Labs

Here, at Quansight Labs, our goal is to work on sustaining the future of Open Source. We make sure we can live up to that goal by spending a significant amount of time working on impactful and critical infrastructure and projects within the Scientific Ecosystem.

As such, our goals align with those of the Chan Zuckerberg Initiative and, in particular, the Essential Open Source Software for Science (EOSS) program that supports tools essential to biomedical research via funds for software maintenance, growth, development, and community engagement.

CZI’s Essential Open Source Software for Science program supports software maintenance, growth, development, and community engagement for open source tools critical to science. And the Chan Zuckerberg Initiative was founded in 2015 to help solve some of society’s toughest challenges — from eradicating disease and improving education, to addressing the needs of our local communities. Their mission is to build a more inclusive, just, and healthy future for everyone.

Today, we are thrilled to announce

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Anaconda Blog 2021-08-30 13:00:00

Pyston Team Joins Anaconda to Expand Open-Source Project Development

We’re optimistic about the potential for Pyston to improve the Python experience for all users and reduce the costs of deploying Python applications at scale. Keep your eyes on this space for future announcements about the Pyston roadmap and other Anaconda initiatives to advance scalable computing in Python.
neptune.ai 2021-08-27 07:04:00

Training and Debugging Deep Convolutional Generative Adversarial Networks

Adversarial networks (Deep Convolutional Generative Adversarial Networks) have been a very active playground lately for Deep Learning practitioners. The field of adversarial networks was established by Ian Goodfellow and his colleagues from the University of Montreal in their article Generative Adversarial Nets. Since then, new variants of the original model keep being developed and research […]

The post Training and Debugging Deep Convolutional Generative Adversarial Networks appeared first on neptune.ai.

neptune.ai 2021-08-26 09:01:41

Best Data Lineage Tools

Data is the main part of every machine learning model. Your model is only as good as the data it’s built with, and you can’t build a model at all without data. So, it makes sense to train your models only with accurate and authentic data. In the course of running their operations, many organizations […]

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neptune.ai 2021-08-25 15:47:54

Data Lineage in Machine Learning: Methods and Best Practices

Data is supposed to be an organization’s most treasured asset. However, it wasn’t this way until recently, so very few people have experience in handling data and leveraging it to create more value. As managers are becoming more data-fluent, many organizations are adopting the practice of tracking data lineage, which has become steady support for […]

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Pierre de Buyl's homepage - scipy 2021-08-24 13:00:00

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.

Quansight Labs 2021-08-18 00:01:00

Is GitHub Actions suitable for running benchmarks?

Benchmarking software is a tricky business. For robust results, you need dedicated hardware that only runs the benchmarking suite under controlled conditions. No other processes! No OS updates! Nothing else! Even then, you might find out that CPU throttling, thermal regulation and other issues can introduce noise in your measurements.

So, how are we even trying to do it on a CI provider like GitHub Actions? Every job runs in a separate VM instance with frequent updates and shared resources. It looks like it would just be a very expensive random number generator.

Well, it turns out that there is a sensible way to do it: relative benchmarking. And we know it works because we have been collecting stability data points for several weeks.

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Anaconda Blog 2021-08-17 13:00:00

How OpenEye Scientific Leverages Anaconda to Power its Cloud-Native Molecular Design Platform

Anaconda provides OpenEye Scientific with a reliable solution for Python Packaging Management in its Orion platform. Our tools enable Orion to utilize Python environments and provide essential features, including computation, storage, analysis, and more. Having easy access to scientific libraries is a powerful benefit for scientific developers working with Orion, and without Anaconda, this would not be feasible. Additionally, our seamless user experience plays an invaluable role in contributing to Orion’s Cloud-Native Molecular Design Platform. At Anaconda, we’re happy to provide a tool that supports growth in the scientific community.
Blog – Enthought 2021-08-10 15:44:41

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 …
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Anaconda Blog 2021-08-05 18:15:00

From Agriculture to Art, Four Unexpected Ways Data Science is Improving our World

The application of data science to a broad set of fields can improve innovation across the whole realm of human experience. Highlighting just a few ways data science has already contributed to these advances makes us even more optimistic for the eventual impact that ongoing breakthroughs in the data profession will have. At Anaconda, we’re excited to continue our support of this innovation. We can’t wait to see the unique and compelling ways data science will continue to improve our world.
Anaconda Blog 2021-07-29 14:30:00

State of Data Science 2021: Becoming “Essential,” Though Untapped Potential Remains

Among the many new concepts introduced to us throughout 2020 was the idea of the “essential worker.” What jobs or roles are so integral to the functioning of a business that the company wouldn’t be able to operate without them? The idea of “essentialness” provides a helpful framework to consider how effectively data science has integrated with an organization’s most important operations.
Quansight Labs 2021-07-25 18:00:00

Moving SciPy to the Meson build system

Let's start with an announcement: SciPy now builds with Meson on Linux, and the full test suite passes!

This is a pretty exciting milestone, and good news for SciPy maintainers and contributors - they can look forward to much faster builds and a more pleasant development experience. So how fast is it? Currently the build takes about 1min 50s (a ~4x improvement) on my 3 year old 12-core Intel CPU (i9-7920X @ 2.90GHz):

Profiling result of a parallel build (12 jobs) of SciPy with Meson. Visualization created with ninjatracing and Perfetto.

As you can see from the tracing results, building a single C++ file (bsr.cxx, which is one of SciPy's sparse matrix formats) takes over 90 seconds. So the 1min 50 sec build time is close to optimal - the only ways to improve it are major surgery on that C++ code, or buying a faster CPU.

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Blog – Enthought 2021-07-23 13:25:20

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 …
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Living in an Ivory Basement 2021-07-19 22:00:00

A biotech career panel in the DIB Lab

Careers outside of universities!

Quansight Labs 2021-07-16 19:50:33

Introducing PyTorch-Ignite's Code Generator v0.2.0

Authors: Jeff Yang, Taras Savchyn, Priyansi, Victor Fomin

Along with the PyTorch-Ignite 0.4.5 release, we are excited to announce the new release of the web application for generating PyTorch-Ignite's training pipelines. This blog post is an overview of the key features and updates of the Code Generator v0.2.0 project release.

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Anaconda Blog 2021-07-15 15:25:00

A Python Data Scientist’s Guide to the Apple Silicon Transition

The M1 Macs are an exciting opportunity to see what laptop/desktop-class ARM64 CPUs can achieve. For general usage, the performance is excellent, but these systems are not aimed at the data science and scientific computing user yet. If you want an M1 for other reasons, and intend to do some light data science, they are perfectly adequate. For more intense usage, you’ll want to stick with Intel Macs for now, but keep an eye on both software development as compatibility improves and future ARM64 Mac hardware, which likely will remove some of the constraints we see today.
Sparrow Computing 2021-07-08 16:09:47

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

The post Poetry for Package Management in Machine Learning Projects appeared first on Sparrow Computing.

Anaconda Blog 2021-07-07 21:05:00

The Benefits of Mirroring the Anaconda Repository

If you’ve decided mirroring is the right choice for your organization, what’s next?
Quansight Labs 2021-07-07 10:00:00

Pyflyby: Improving Efficiency of Jupyter Interactive Sessions

Few things hinder productivity more than interruption. A notification, random realization, or unrelated error can derail one's train of thought when deep in a complex analysis – a frustrating experience.

In the software development context, forgetting to import a statement in an interactive Jupyter session is such an experience. This can be especially frustrating when using typical abbreviations, like np, pd, plt, where the meaning is obvious to the human reader, but not to the computer. The time-to-first-plot, and ability to quickly cleanup one's notebook afterward are critical to an enjoyable and efficient workflow.

In this blogpost we present pyflyby, a project and an extension to IPython and JupyterLab, that, among many things, automatically inserts imports and tidies Python files and notebooks.

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Sparrow Computing 2021-06-29 20:38:29

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

The post Development containers in VS Code: a quick start guide appeared first on Sparrow Computing.

Anaconda Blog 2021-06-29 12:00:00

Scikit-learn Speed-up with Intel and Anaconda

Notices & Disclaimers ​
Living in an Ivory Basement 2021-06-28 22:00:00

New sourmash databases are available!

Databases are now available for GTDB!

Quansight Labs 2021-06-28 08:00:00

Distributed Training Made Easy with PyTorch-Ignite

Authors: François Cokelaer, Priyansi, Sylvain Desroziers, Victor Fomin

Writing agnostic distributed code that supports different platforms, hardware configurations (GPUs, TPUs) and communication frameworks is tedious. In this blog, we will discuss how PyTorch-Ignite solves this problem with minimal code change.

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Filipe Saraiva's blog 2021-06-25 12:06:45

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 Piauí
Anaconda Blog 2021-06-24 15:19:00

How to Get Involved in Open Source: A Roadmap for Beginners

Are you looking for more tips on making your first open-source contribution? Watch our tutorial for more details on getting started.
Quansight Labs 2021-06-23 18:35:06

Working with pytest on PyTorch

Prerequisites

To run the code in this post yourself, make sure you have torch, ipytest>0.9, and the plugin to be introduced pytest-pytorch installed.

pip install torch 'ipytest>0.9' pytest-pytorch

Before we start testing, we need to configure ipytest. We use the ipytest.autoconfig() as base and add some pytest CLI flags in order to get a concise output.

In [1]:
import ipytest

ipytest.autoconfig(defopts=False)

default_flags = ("--quiet", "--disable-warnings")

def _configure_ipytest(*additional_flags, collect_only=False):
    addopts = list(default_flags)
    if collect_only:
        addopts.append("--collect-only")
    addopts.extend(additional_flags)
    
    ipytest.config(addopts=addopts)

def enable_pytest_pytorch(collect_only=False):
    _configure_ipytest(collect_only=collect_only)
    
def disable_pytest_pytorch(collect_only=False):
    _configure_ipytest("--disable-pytest-pytorch", collect_only=collect_only)
    
disable_pytest_pytorch()

If you work on PyTorch and like pytest you may have noticed that you cannot run some tests in the test suite using the default pytest double colon syntax {MODULE}::TestFoo::test_bar.

In [2]:
%%run_pytest[clean] {MODULE}::TestFoo::test_bar

from
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Blog – Enthought 2021-06-23 16:27:51

Lessons 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 …
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Blog – Enthought 2021-06-22 13:27:21

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 …
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Filipe Saraiva's blog 2021-06-21 21:51:57

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 Computacional
Anaconda Blog 2021-06-18 12:54:00

I Have an Anaconda Nucleus Account - Now What?

Expect more from Anaconda Nucleus in the coming months! Our goals include onboarding content partners, hosting events, and connecting practitioners with each other.
Blog – Enthought 2021-06-15 19:54:08

Enthought 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 …
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AI Pool Articles 2021-06-08 18:19:38

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.
Living in an Ivory Basement 2021-06-07 22:00:00

Searching all public metagenomes with sourmash

Searching all the things!

Quansight Labs 2021-05-25 08:00:00

Putting out the fire: Where do we start with accessibility in JupyterLab?

JupyterLab Accessibility Journey Part 2

I want to be honest with you, I started asking accessibility questions in JupyterLab spaces while filled with anxiety. Anxiety that I was shouting into the void and no one else would work on accessibility with me. Anxiety that I didn’t have the skills or energy or knowledge to back up what I wanted to do. Anxiety that I was going to do it wrong and make JupyterLab even more inaccessible. Sometimes I still feel that way.

Read more… (6 min remaining to read)

AI Pool Articles 2021-05-24 16:10:20

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.
Pierre de Buyl's homepage - scipy 2021-05-21 13:00:00

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.

AI Pool Articles 2021-05-29 13:40:17

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.
Living in an Ivory Basement 2021-05-16 22:00:00

sourmash 4.1.0 released!!

sourmash v4.1.0 is here!

AI Pool Articles 2021-05-15 12:19:22

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.
AI Pool Articles 2021-05-15 10:22:56

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 package
Sparrow Computing 2021-05-14 20:11:16

Basic 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

The post Basic Counting in Python appeared first on Sparrow Computing.

AI Pool Articles 2021-05-14 16:19:07

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 it
AI Pool Articles 2021-05-14 16:15:32

Decision Trees

Intuition and implementation of the first tree-based algorithm in machine learning
AI Pool Articles 2021-05-14 16:01:47

Dimensionality 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.
AI Pool Articles 2021-05-13 18:17:40

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.
Sparrow Computing 2021-05-13 18:11:11

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

The post How to Use the PyTorch Sigmoid Operation appeared first on Sparrow Computing.

AI Pool Articles 2021-05-13 16:07:08

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 explained
AI Pool Articles 2021-05-11 17:24:10

Understanding of Regularization in Neural Networks

This article includes the different techniques of regularization like Data Augmentation, L1, L2, Dropout, and Early Stopping
AI Pool Articles 2021-05-10 18:04:00

Diving 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.
AI Pool Articles 2021-05-10 18:03:29

Normalization in Deep learning

Different types of Normalization in Deep Learning. A very useful technique to avoid overfitting and generalize your model better.
AI Pool Articles 2021-05-10 18:03:08

Dropout in Deep Learning

Understanding Dropouts in Deep Learning to reduce overfitting
AI Pool Articles 2021-05-10 18:02:37

Yolov3 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 detection
AI Pool Articles 2021-05-10 18:02:03

End-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.
AI Pool Articles 2021-05-10 18:00:28

Supervised learning with Scikit-Learn Library

How to create a model for supervised learning like linear and logistic regression with scikit-learn python library
AI Pool Articles 2021-05-10 18:00:13

Linear and Logistic Regression

Intuition and implementation behind the base algorithms for supervised machine learning
AI Pool Articles 2021-05-10 17:59:02

Random Forests Understanding

Intuition and Implementation on a key algorithm to reduce overfitting in tree based algorithms
AI Pool Articles 2021-05-10 17:57:58

Activation Functions for Neural Networks

In this article, explaination of various activation functions has been given like Linear, ELU, ReLU, Sigmoid, and tanh.
Blog – Enthought 2021-05-06 12:12:46

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 …
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fa.bianp.net 2021-04-12 22:00:00

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 2021-04-09 18:02:05

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 […]

The post NumFOCUS Welcomes Tesco Technology to Corporate Sponsors appeared first on NumFOCUS.

NumFOCUS 2021-04-08 21:14:55

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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Acoular 2021-04-01 05:00:00

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.
Acoular 2021-04-01 05:00:00

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.
Acoular 2021-04-01 05:00:00

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...)
Blog – Enthought 2021-03-24 18:55:46

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   …
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Sparrow Computing 2021-03-22 23:54:00

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.

Sparrow Computing 2021-03-20 03:15:00

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.

Blog – Enthought 2021-03-09 18:37:16

Giving Visibility to Renewable Energy

The EnergizAIR Infrastructure framework and key interfaces, with the Enthought responsibility on the project shown in the central, grey box. The ultimate project goal was to raise individual awareness of the contribution of renewable energy sources, and ultimately change behaviors. Now ten years later, with orders of magnitude more data, AI/machine learning, cloud, and smartphones …
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Sparrow Computing 2021-03-03 17:10:00

NumPy Where: Understanding np.where()

The NumPy where function is like a vectorized switch that you can use to combine two arrays.

The post NumPy Where: Understanding np.where() appeared first on Sparrow Computing.

Sparrow Computing 2021-03-02 14:05:00

Finding the Mode of an Empirical Continuous Distribution

You can find the mode of an empirical continuous distribution by plotting the histogram and looking for the maximum bin.

The post Finding the Mode of an Empirical Continuous Distribution appeared first on Sparrow Computing.

fa.bianp.net 2021-03-01 23:00:00

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 *

Sparrow Computing 2021-02-25 14:03:00

NumPy All: Understanding np.all()

The np.all() function tests whether all elements in a NumPy array evaluate to true.

The post NumPy All: Understanding np.all() appeared first on Sparrow Computing.

While My MCMC Gently Samples 2021-02-23 15:00:00

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 …

Sparrow Computing 2021-02-22 13:57:00

Binary Cross Entropy Explained

A simple NumPy implementation of the binary cross entropy loss function and some intuition about why it works.

The post Binary Cross Entropy Explained appeared first on Sparrow Computing.

Martin Fitzpatrick - python 2021-02-22 08:00:00

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 …

Blog – Enthought 2021-02-16 20:16:34

SciPy 2021

As in 2020, this year’s SciPy Conference will be virtual, offering increased opportunities for attendance. 2020 set an attendance record of over 1,500, almost double the 2019 Austin, Texas conference. The event brings together attendees from industry, academia, national labs and more – showcasing projects, sharing knowledge and collaborating on code development.   Author: Kristen Leiser, …
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NumFOCUS 2021-02-10 19:54:10

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 […]

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Living in an Ivory Basement 2021-02-01 23:00:00

Transition your Python project to use pyproject.toml and setup.cfg! (An example.)

Updating old Python packages, in this year of the PSF 2021!

Martin Fitzpatrick - python 2021-01-28 14:00:00

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 …

Living in an Ivory Basement 2021-01-24 23:00:00

A snakemake hack for checkpoints

snakemake checkpoints r awesome

Martin Fitzpatrick - python 2021-01-21 07:00:00

micro:bit Space Invaders — MicroPython retro game in just 25 pixels

How much game can you fit into 25 pixels? Quite a bit it turns out.

This is a mini clone of arcade classic Space Invaders for the BBC micro:bit microcomputer. Using the accelerometer and two buttons for input, to can beat off wave after wave of aliens that advance …

ListenData 2021-01-06 10:35:00

Run SAS in Python without Installation

Introduction
In the past few years python has gained a huge popularity as a programming language in data science world. Many banks and pharma organisations have started using Python and some of them are in transition stage, migrating SAS syntax library to Python. Many big organisations have been using SAS since early 2000 and they developed a hundreds of SAS codes for various tasks ranging from data extraction to model building and validation. Hence it's a marathon task to migrate SAS code to any other programming language. Migration can only be done in phases so day to day tasks would not be hit by development and testing of python code. Since Python is open source it becomes difficult sometimes in terms of maintaining the existing code. Some SAS procedures are very robust and powerful in nature its alternative in Python is still not implemented, might be doable but not a straightforward way for average developer or analyst.

Do you wish

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