Mestrado em Ciência da Computação 2022: Metaheurísticas

Estamos ainda com algumas vagas abertas para o Mestrado em Ciência da Computação na UFPA, Belém. Os interessados, favor olhar as instruções para submissão na página de seleção do programa. Desde meu ingresso no programa venho orientando alunos em diferentes pesquisas sobre inteligência computacional aplicados a problemas de smart grids. Já tivemos trabalhos sobre sistemas multiagentes… Continue a ler »Mestrado em Ciência da Computação 2022: Metaheurísticas

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Make Your Model Faster with PyTorch Lightning

In this article we will discuss how to make your training and inference processes realy fast using pytorch lightning.

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The Best Vertex ML Metadata Alternatives

Last year, Google announced Vertex AI, a new managed machine learning platform aimed at making it easier for developers to deploy and maintain AI models. So, what are the benefits and drawbacks of Vertex AI? In this article, we’ll discuss Vertex AI and take a look at its alternatives, but first, let’s see why is […]

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Best DataRobot Alternatives for Model Registry

DataRobot is an enterprise AI platform that provides tools for building, managing, and deploying models at scale for businesses, and automating end-to-end ML lifecycles. It contains different kinds of models, data, latest open-source algorithms that can be used on-premise, or as a fully managed AI service, DataRobot gives you the power of AI to drive […]

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ML Model Testing: 5 Teams Share How They Test Their Models

Despite the progress of the machine learning industry in developing solutions that help data teams and practitioners operationalize their machine learning models, testing these models to make sure they’ll work as intended remains one of the most challenging aspects of putting them into production.  Most processes used to test ML models for production usage are […]

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Introducing Anaconda’s Maker Blog Series!

Before we wrap up the first month of 2022, we’d like to announce an exciting blog series coming to Anaconda.com! Beginning in February, we will feature blog posts authored by members of our cherished Maker community on a monthly basis.

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How to Deal With Imbalanced Classification and Regression Data

Data imbalance is predominant and inherent in the real world. Data often demonstrates skewed distributions with a long tail. However, most of the machine learning algorithms currently in use were designed around the assumption of a uniform distribution over each target category (classification).  On the other hand, we must not forget that many tasks involve […]

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Distributed Training: Frameworks and Tools

Recent developments in deep learning have led to some fascinating state-of-the-art results especially in the areas like natural language processing and computer vision. A couple of the reasons for the success usually comes from the availability of a huge amount of data and the increasing size of deep learning (DL) models. These algorithms are capable […]

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DiffCast: Automatic Python Screencast Creator — Create reproducible programming screencasts without typos or edits

Programming screencasts are a popular way to teach programming and demo tools. Typically people will open up their favorite editor and record themselves tapping away. But this has a few problems. A good setup for coding isn't necessarily a good setup for video -- with text too small, a window too …

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Anaconda is Launching Long-Term Support

Anaconda’s mission is multifaceted, and one of our core tenants is to steward open-source innovation by fostering communities and championing open standards for data and computation.

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The Best Amazon SageMaker Alternatives [for Experiment Tracking and Model Management]

Machine learning projects are far more complex than a typical software project. Compared to a software project, which primarily involves writing and testing some code, ML projects follow an iterative process and require intense experimentation. More than a code error, ML projects fail due to concept drift, poorly tuned values of hyperparameters, and faulty model […]

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Python Type Isinstance

Python Type Isinstance

Instance and Type both are used to check the type of object. Instance can check the type of subclass too where as type can't.

In [4]:

!python --version

Python 3.6.10 :: Anaconda, Inc.

type in Python

Check if type is Integer

In [1]:

x = 1

In [6]:

print(type(x))

<class 'int'>

Check if type is Float

In [8]:

x = 1.5

In [9]:

print(type(x))

<class 'float'>

Check if type is String

In [10]:

x = 'john'

In [11]:

print(type(x))

<class 'str'>

In [12]:

x = 'j'

In [13]:

print(type(x))

<class 'str'>

Check if type is Class

In [14]:

class demo():
    pass

In [22]:

print(type(demo()))

<class '__main__.demo'>

In [23]:

type(demo())==demo

Out[23]:

True

isinstance in Python

isinstance can be used to check the type of object.

In [24]:

x = 1

In [25]:

isinstance(x,int)

Out[25]:

True

In [26]:

isinstance(x,float)

Out[26]:

False

In [27]:

x = 1.2

In [28]:

isinstance(x,float)

Out[28]:

True

isinstance can check the

(continued...)

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Distributed Training: Guide for Data Scientists

Have you ever wondered how complex models with millions to billions of parameters are trained on terabytes of data? In fact, the size of such models can get so large that they may not even fit in the memory of a single processor. Thus training such models becomes impossible via conventional means and we need […]

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Conda and Grayskull, the Masters of Software Packaging

Python might be the most popular snake out there, but most of us have also heard of that other serpent: Conda. And some of us have wondered what it really is. In this post we’ll learn about Conda, software packages and package recipes. Most importantly we’ll learn about Grayskull — a conda recipe generator.

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On minimum metagenome covers, and calculating them for your own data.

You, too, can run our software!

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5 Ways Machine Learning Teams Use CI/CD in Production

One of the core concepts in DevOps that is now making its way to machine learning operations (MLOps) is CI/CD—Continuous Integration and Continuous Delivery or Continuous Deployment. CI/CD as a core DevOps practice embraces tools and methods to deliver software applications reliably by streamlining the building, testing, and deployment of your applications to production. Let’s […]

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IPython 8.0, Lessons learned maintaining software

This is a companion post from the Official release of IPython 8.0, that describe what we learned with this large new major IPython release. We hope it will help you apply best practices, and have an easier time maintaining your projects, or helping other. We'll focus on many patterns that made it easier for us to make IPython 8.0 what it is with minimal time involved.

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A New Way To Connect with Other Anaconda Users

Back in December 2021, we launched the Anaconda Community, our first-ever space for users to get insights into the newest developments in the world of data, get "unstuck" where they may have a problem, and reach out for technical help. It also allows individuals to engage with other professionals and ask questions to the broader data community.

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When to Choose CatBoost Over XGBoost or LightGBM [Practical Guide]

Boosting algorithms have become one of the most powerful algorithms for training on structural (tabular) data. The three most famous boosting algorithm implementations that have provided various recipes for winning ML competitions are: In this article, we will primarily focus on CatBoost, how it fares against other algorithms and when you should choose it over […]

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Optimization Nuggets: Implicit Bias of Gradient-based Methods

When an optimization problem has multiple global minima, different algorithms can find different solutions, a phenomenon often referred to as the implicit bias of optimization algorithms. In this post we'll characterize the implicit bias of gradient-based methods on a class of regression problems that includes linear least squares and Huber …

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