Planet SciPy
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.Predicting Stock Prices Using Machine Learning
The stock market is known for being volatile, dynamic, and nonlinear. Accurate stock price prediction is extremely challenging because of multiple (macro and micro) factors, such as politics, global economic conditions, unexpected events, a company’s financial performance, and so on. But, all of this also means that there’s a lot of data to find patterns […]
The post Predicting Stock Prices Using Machine Learning appeared first on neptune.ai.
10 NLP Projects to Boost Your Resume
Natural Language Processing (NLP) is a very exciting field. Already, NLP projects and applications are visible all around us in our daily life. From conversational agents (Amazon Alexa) to sentiment analysis (Hubspot’s customer feedback analysis feature), language recognition and translation (Google Translate), spelling correction (Grammarly), and much more. Whether you’re a developer or data scientist […]
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How To Calculate Stocks Support And Resistance Using Clustering
In this notebook, I will show you how to calculate Stocks Support and Resistance using different clustering techniques.
Stock Data - I have stocks data in mongo DB. You can also get this data from Yahoo Finance for free.
import pymongo from pymongo import MongoClient client_remote = MongoClient('mongodb://localhost:27017') db_remote = client_remote['stocktdb'] collection_remote = db_remote.stock_data
I will do this analysis using last 60 days of Google data.
mobj = collection_remote.find({'ticker':'GOOGL'}).sort([('_id',pymongo.DESCENDING)]).limit(60)
I will be using Pandas and Numpy for the data manipulation. Let us first get the data from Mongo Cursor object to Python list.
prices = [] for doc in mobj: prices.append(doc['high'])
import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.cluster import AgglomerativeClustering
For K means clustering, we need to get the data in
How to Kick Off a Machine Learning Project With Less Data
If machine learning solutions were cars, their fuel would be data. Simply viewed, ML models are statistical equations that need values and variables to operate, and the data is the biggest contributor to ML success. Today, sources of data are ample, and the amount of available data keeps growing exponentially. This allows us to wrangle, […]
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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.
Read more… (14 min remaining to read)
Natural Language Processing with Hugging Face and Transformers
NLP is a branch of machine learning that is about helping computers and intelligent systems to understand text and spoken words in the same way that humans do. NLP drives computer programs to perform a wide range of incredibly useful tasks, like text translation, responding to spoken commands, or summarizing large volumes of text in […]
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How to Work with Autoencoders [Case Study Guide]
Autoencoders are a class of neural networks that are used in unsupervised learning tasks. They have two neural networks components: Encoder and Decoder. Both components have essentially the same configurations, which means that the shape of the input will be similar to the shape of the output, and also the input will be the same […]
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Installing MuJoCo to Work With OpenAI Gym Environments
In this article, I’ll show you how to install MuJoCo on your Mac/Linux machine in order to run continuous control environments from OpenAI’s Gym. These environments include classic ones like HalfCheetah, Hopper, Walker, Ant, and Humanoid and harder ones like object manipulation with a robotic arm or robotic hand dexterity. I’ll also discuss additional agent […]
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A biotech career panel in the DIB Lab
Careers outside of universities!
15 Computer Visions Projects You Can Do Right Now
Computer vision deals with how computers extract meaningful information from images or videos. It has a wide range of applications, including reverse engineering, security inspections, image editing and processing, computer animation, autonomous navigation, and robotics. In this article, we’re going to explore 15 great OpenCV projects, from beginner-level to expert-level. For each project, you’ll see […]
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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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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.In-depth Guide to ML Model Debugging and Tools You Need to Know
Everyone is excited about machine learning, but only a few know and understand the limitations that keep ML from widespread adoption. ML models are great at specific tasks, but they can also get a lot of things wrong. The key to a successful project is understanding how your model(s) can fail, and preparing appropriate solutions […]
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ML from Research to Production – Challenges, Best Practices and Tools [Guide]
Taking machine learning or AI into production takes a lot of patience, effort, and resources. AI models are great for predicting all sorts of things, from what movie you’ll like to whether your cat will scratch the furniture. But in most cases, AI models have a hard time making it into production. In this article, […]
The post ML from Research to Production – Challenges, Best Practices and Tools [Guide] appeared first on neptune.ai.
How to Scale ML Projects – Lessons Learned from Experience
In the past decade, machine learning has been responsible for turning data into the most valuable asset for an organization. Building out business solutions with ML applications using vast datasets is easier than ever thanks to advancements in computational cloud infrastructures where you can host and run ML algorithms. At the enterprise level, ML models […]
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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
The post Poetry for Package Management in Machine Learning Projects appeared first on Sparrow Computing.
The Benefits of Mirroring the Anaconda Repository
If you’ve decided mirroring is the right choice for your organization, what’s next?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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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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Scikit-learn Speed-up with Intel and Anaconda
Notices & Disclaimers New sourmash databases are available!
Databases are now available for GTDB!
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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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 PiauíHow 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.Working with pytest on PyTorch
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-pytorchBefore 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.
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()
%%run_pytest[clean] {MODULE}::TestFoo::test_bar from
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 …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 ComputacionalI 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.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 …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!
How Businesses Can Support Open-Source Communities
Businesses can support the broader open-source community, rather than just individual projects, by investing in things like conferences and access to hardware for different developer groups. This helps strengthen the community as a whole and supports the overall innovation commons. For example, one way we’ve chosen to do this at Anaconda is through the Anaconda Dividend Program. It works by donating 1% of our Commercial Edition revenue to the NumFOCUS organization to support various open-source projects and programs. Although we continue to also contribute to projects on a more ad-hoc basis, we felt it was important to establish a way to invest in open source that would support the sustainable, long-term growth of the innovation commons.The Ongoing Journey of AAPI Representation in Tech
This year’s Asian American and Pacific Islander Heritage Month comes at a pivotal time for the community. On the one hand, there has never been more public dialogue about diversity and inclusion, including those issues affecting the AAPI community. Yet, we have also seen a troubling rise in violence and prejudice against Asian Americans in recent months. While there are no simple solutions to these challenges, AAPI Heritage month provides an opportunity to uplift AAPI voices and encourage Asian Americans to share their stories. As a first-generation Chinese American and co-founder of Anaconda, I believe I am responsible for sharing my perspective, particularly about the Asian American experience in the technology field.Putting out the fire: Where do we start with accessibility in JupyterLab?
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)
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.
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.Anaconda Brings Data Science to Linux on IBM Z and LinuxONE
Today, along with IBM, we are excited to announce the general availability of Anaconda for Linux on Z and LinuxONE. Anaconda on Linux on Z and LinuxONE is our latest step toward expanding the availability of key open-source data science tools across platforms and improving the experience for practitioners everywhere.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
The post Basic Counting in Python appeared first on Sparrow Computing.
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.Announcing Anaconda Support on AWS Graviton2
Today, Anaconda, the world’s most popular Python distribution platform for data science and machine learning, in collaboration with AWS, is happy to announce the general availability of Anaconda for Linux on the aarch64 (arm64) platform optimized for Amazon Web Services’ Graviton2 processors.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
The post How to Use the PyTorch Sigmoid Operation appeared first on Sparrow Computing.
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.Rethinking Jupyter Interactive Documentation
Jupyter Notebook first release was 8 years ago – under the IPython Notebook name at the time. Even if notebooks were not invented by Jupyter; they were definitely democratized by it. Being Web powered allowed development of many changes in the Datascience world. Objects now often expose rich representation; from Pandas dataframes with as html tables, to more recent Scikit-learn model.
Today I want to look into a topic that has not evolved much since, and I believe could use an upgrade. Accessing interactive Documentation when in a Jupyter session, and what it could become. At the end I'll link to my current prototype if you are adventurous.
Read more… (7 min remaining to read)
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
Spot the differences: what is new in Spyder 5?
In case you missed it, Spyder 5 was released at the beginning of April! This blog post is a conversation attempting to document the long and complex process of improving Spyder's UI with this release. Portions lead by Juanita Gomez are marked as Juanita, and those lead by Isabela Presedo-Floyd are marked as Isabela.
What did we do?[Juanita] Spyder was created more than 10 years ago and it has had the contributions of a great number of developers who have written code, proposed ideas, opened issues and tested PRs in order to build a piece of Spyder on their own. We (the Spyder team) have been lucky to have such a great community of people contributing throughout the years, but this is the first time that we decided to ask for help from an UX/UI expert! Why? You might wonder. Having the contributions of this great amount of people has resulted in inconsistencies around Spyder’s interface which we didn’t stop
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 …
A step towards educating with Spyder
As a community manager in the Spyder team, I have been looking for ways of involving more users in the community and making Spyder useful for a larger number of people. With this, a new idea came: Education.
For the past months, we have been wondering with the team whether Spyder could also serve as a teaching-learning platform, especially in this era where remote instruction has become necessary. We submitted a proposal to the Essential Open Source Software for Science (EOSS) program of the Chan Zuckerberg Initiative, during its third cycle, with the idea of providing a simple way inside Spyder to create and share interactive tutorials on topics relevant to scientific research. Unfortunately, we didn’t get this funding, but we didn’t let this great idea die.
We submitted a second proposal to the Python Software Foundation from which we were awarded $4000. For me, this is the perfect opportunity for us to take the first step towards using Spyder for education.
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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.
PyTorch TensorIterator Internals - 2021 Update
For contributors to the PyTorch codebase, one of the most commonly encountered
C++ classes is
TensorIterator.
TensorIterator offers a standardized way to iterate over elements of
a tensor, automatically parallelizing operations, while abstracting device and
data type details.
In April 2020, Sameer Deshmukh wrote a blog article discussing PyTorch TensorIterator Internals. Recently, however, the interface has changed significantly. This post describes how to use the current interface as of April 2021. Much of the information from the previous article is directly copied here, but with updated API calls and some extra details.
Read more… (8 min remaining to read)
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 […]
The post Job Posting | Communications and Marketing Manager appeared first on NumFOCUS.
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.
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 …Continue Reading
sourmash 4.0 is now available! Low low cost if you buy now!
sourmash v4.0.0 is here!
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.
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.
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 *
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.
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 …
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.
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!
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, …Continue Reading
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.
Strategy, Digitalization and Global Trends – C Suite Reflections
Key points are presented from the first of a series of LinkedIn articles where JSR Board Chairman Mitsunobu Koshiba (‘Nobu’) provides thought provoking insights on business strategy in the context of trends in three time horizons. The short term is dominated by an increased acceptance of Modern Monetary Theory. The mid-term is a shift in …Continue Reading
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 …
A snakemake hack for checkpoints
snakemake checkpoints r awesome
SAM Coupé Reader — Preserving FRED retro disk magazine text, by decoding the Entropy Reader
FRED was the most popular disk magazine for the SAM Coupé 8 bit home computer.Published by Colin MacDonald out of sunny Monifieth, Scotland, the magazine ran from it's first issue in 1990 through to it's last (82) in 1998.
For the SAM networking project I was hoping there might …
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 …
Run SAS in Python without Installation
Do you wish
(continued...)Disnatia X/Potências de X
Nenhuma equipe de heróis me é tão querida quanto X-Men. Lá pelo final dos anos 90 comecei a colecionar por alguns anos, mas em seguida veio o fatídico aumento de preço com as Super-Heróis Premium, o que me acabou desmotivando a comprar. De lá para cá, acompanho esporadicamente, lendo notícias sobre, comprando uma ou outra… Continue a ler »Disnatia X/Potências de XWish Christmas with Python and R
This post is dedicated to all the Python and R Programming Lovers...Flaunt your knowledge in your peer group with the following programs. As a data science professional, you want your wish to be special on eve of christmas. If you observe the code, you may also learn 1-2 tricks which you can use later in your daily tasks.
Method 1 : Run the following program and see what I mean
R Code
paste(intToUtf8(acos(log(1))*180/pi-13),
toupper(substr(month.name[2],2,2)),
paste(rep(intToUtf8(acos(exp(0)/2)*180/pi+2^4+3*2),2), collapse = intToUtf8(0)),
LETTERS[5^(3-1)], intToUtf8(atan(1/sqrt(3))*180/pi+2),
toupper(substr(month.abb[10],2,2)),
intToUtf8(acos(log(1))*180/pi-(2*3^2)),
toupper(substr(month.name[4],3,4)),
intToUtf8(acos(exp(0)/2)*180/pi+2^4+3*2+1),
intToUtf8(acos(exp(0)/2)*180/pi+2^4+2*4),
intToUtf8(acos(log(1))*180/pi-13),
LETTERS[median(0:2)],
intToUtf8(atan(1/sqrt(3))*180/pi*3-7),
sep = intToUtf8(0)
)
Python Code
import math
import datetime
(chr(int(math.acos(math.log(1))*180/math.pi-13)) \
+ datetime.date(1900, 2, 1).strftime('%B')[1] \
+ 2 * datetime.date(1900, 2, 1).strftime('%B')[3] \
+ datetime.date(1900, 2, 1).strftime('%B')[7] \
+ chr(int(math.atan(1/math.sqrt(3))*180/math.pi+2)) \
+ datetime.date(1900, 10, 1).strftime('%B')[1] \
+ chr(int(math.acos(math.log(1))*180/math.pi-18)) \
+ datetime.date(1900, 4, 1).strftime('%B')[2:4] \
+ chr(int(math.acos(math.exp(0)/2)*180/math.pi+2**4+3*2+1)) \
+ chr(int(math.acos(math.exp(0)/2)*180/math.pi+2**4+2*4)) \
+ chr(int(math.acos(math.log(1))*180/math.pi-13)) \
+ "{:c}".format(97) \
+ chr(int(math.atan(1/math.sqrt(3))*180/math.pi*3-7))).upper()
Turn on computer speakers before running the code.
R Code
install.packages("audio")
library(audio)
christmas_file <- tempfile()
download.file("https://sites.google.com/site/pocketecoworld/merrychristmas1.wav", christmas_file, mode = "wb")
xmas
On the Link Between Optimization and Polynomials, Part 2
We can tighten the analysis of momentum methods through Chebyshev polynomials of the first and second kind. Following this connection, we'll derive one of the most iconic methods in optimization: Polyak momentum.
How to use variable in a query in pandas
Suppose you want to reference a variable in a query in pandas package in Python. This seems to be a straightforward task but it becomes daunting sometimes. Let's discuss it with examples in the article below.Let's create a sample dataframe having 3 columns and 4 rows. This dataframe is used for demonstration purpose.
import pandas as pd
df = pd.DataFrame({"col1" : range(1,5),
"col2" : ['A A','B B','A A','B B'],
"col3" : ['A A','A A','B B','B B']
})
A A in column col2@.