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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 this blog, our... 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 your Python application.... Read more
Watch: Introduction to Quant Finance with Quantiacs Toolbox
This presentation by Eric Hamer will take you through the basics of machine learning and quant finance, and illustrate how to create and test machine learning strategies using Quantiacs. The webinar covers: – An Overview of Machine Learning – The Machine Learning Process – Features of the Quantiacs toolkit... Read more
The Rise of Notebooks Extended
I recently had the privilege of presenting a workshop at the AI + Education Curiosity Conference 2019. There, I demonstrated to educators, school district staff, researchers, and students how RAPIDS software enables students to learn and iteratively practice data science using full datasets all within classroom time constraints. Compared to current methods and workarounds,... 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 isn’t that impressive.... 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 testing. Challenges associated... 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 intuitive. If the... Read more
Using RAPIDS with PyTorch
In this post we take a look at how to use cuDF, the RAPIDS dataframe library, to do some of the preprocessing steps required to get the mortgage data in a format that PyTorch can process so that we can explore the performance of deep learning on tabular data and... Read more
How to Get Started with SQL
Just about every popular app and social media platform has an engine under the hood that powers the information and data we absorb. With hundreds of millions of data points to keep track of, there has to be a well-oiled machine to maintain that database. Every developer has their... Read more
Jupyter Notebook: Python or R—Or Both?
I was analytically betwixt and between a few weeks ago. Most of my Jupyter Notebook work is done in either Python or R. Indeed, I like to self-demonstrate the power of each platform by recoding R work in Python and vice-versa. I must have a dozen active notebooks, some... Read more