Swift Versus Python: Common Features, Strengths, and Weaknesses
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++
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?
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
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
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
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
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
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 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
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