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Python

Swift Versus Python: Common Features, Strengths, and Weaknesses

Python Tools & Languages swiftposted by ODSC Community October 18, 2019

There are many popular languages, but not all of them remain popular year after year. Nevertheless, some languages don’t lose their popularity and become even more widespread. We discuss the debate around Swift versus Python. For example, PYPL... Read more

Timing the Same Algorithm in R, Python, and C++

Python R Tools & Languages C++Python Rposted by John Mount September 23, 2019

While developing the RcppDynProg R package I took a little extra time to port the core algorithm from C++ to both R and Python. This means I can time the exact same algorithm implemented nearly identically in each of these three languages. So I can extract some comparative “apples to apples” timings. Please read... Read more

How Do You Do an Arithmetic Expression Using Python?

Python Tools & Languages Arithmetic Expressionposted by Divyesh Dharaiya September 17, 2019

Expressions are portrayals of significant value. They are unique in relation to explanation in the way that announcements work on something while expressions are portrayal of significant value. For instance any string is likewise an expressions since it speaks to the estimation of the string also.... Read more

ODSC West 2019 Preview: Python for Data Acquisition

Blogs from ODSC Speakers Conferences Python Tools & Languages data acquisition python for data acquisition West 2019posted by Phil Tracton September 6, 2019

Editor’s Note: See Phil present his talk “Python for Data Acquisition” at ODSC West 2019. What does it take, on the technical side, to get a project started? After you have an idea and find something you want to study or look into, you need to... Read more

Building a Data Pipeline in Python – Part 2 of N – Data Exploration

Python Tools & Languages data pipeline Pythonposted by Scott Stoltzman September 6, 2019

Initial data acquisition and data analysis In order to get an idea of what our data looks like, we need to look at it! The Jupyter Notebook, embedded below, will show steps to load your data into Python and find some basic statistics to use them... Read more

Introduction to IBM Assistant

Python Tools & Languages ibm assistantposted by Divyesh Dharaiya July 16, 2019

IBM Assistant is a chatbot service that many companies are deploying either on their websites or their portal. IBM Watson is providing cloud services, one of them is to build chatbots and you can deploy it either on the website or make a window application. In... Read more

Watch: Kubeflow and Beyond: Automation of Model Training, Deployment and Testing

Machine Learning Modeling Pythonposted by ODSC Team July 12, 2019

Very often a workflow of training models and delivering them to the production environment contains loads of manual work. Those could be either building a Docker image and deploying it to the Kubernetes cluster or packing the model to the Python package and installing it to... Read more

GPU Dask Arrays, First Steps Throwing Dask and CuPy Together

Modeling Python Tools & Languagesposted by RAPIDS July 4, 2019

The following code creates and manipulates 2 TB of randomly generated data. On a single CPU, this computation takes two hours. On an eight-GPU single-node system this computation takes nineteen seconds. Combine Dask Array with CuPy Actually this computation... Read more

How to Leverage Pre-Trained Layers in Image Classification

Deep Learning Modeling Python leverage pre-trained layersposted by Nathaniel Jermain June 28, 2019

Deep learning models like convolutional neural networks (ConvNet) require large amounts of data to make accurate predictions. In general, sufficient sample size for a ConvNet application would involve tens of thousands of images. Often, only a few thousand labeled images are available for training, validation, and... Read more

Image Augmentation for Convolutional Neural Networks

Machine Learning Modeling Python Convolutional Neural Networks Machine Learning Pythonposted by Nathaniel Jermain June 26, 2019

Limited data is a major obstacle in applying deep learning models like convolutional neural networks. Often, imbalanced classes can be an additional hindrance; while there may be sufficient data for some classes, equally important, but undersampled classes will suffer from poor class-specific accuracy. This phenomenon is... Read more