The Interplay of Experimentation and ML to Aid in Repayment of Micro-Loans in Sub-Saharan Africa
Editor’s Note: Brianna is speaking at ODSC West 2019 and ODSC Europe 2019, see her talk “The Interplay of Experimentation and ML to Aid in Repayment of Micro-Loans in Sub-Saharan Africa” there Imagine that through a twist of fate, rather than living the life that brought you to reading this... Read more
Building a Convolutional Neural Network: Male vs Female
In this blog, we are going to classify images using Convolutional Neural Network (CNN), and for deployment, you can use Colab, Kaggle or even use your local machine since the dataset size is not very large. At the end of this, you will be able to build your own... Read more
gQuant — GPU-Accelerated examples for Quantitative Analyst Tasks
gQuant Background: Our prior blog gave a high-level overview of examples in the gQuant repository using GPU accelerated Python. Here we will dive more deeply into the technical details. The examples in gQuant are built on top of NVIDIA’s RAPIDS framework and feature fast data access provided by cuDF dataframes residing in high... Read more
RAPIDS 0.8: Same Community New Freedoms
RAPIDS released 0.8 a few weeks back. And afterwards, like most Americans, we took off for the 4th of July holiday. Over that break, I reflected on the purpose of RAPIDS. Speed is great, building a strong community is awesome, but the true power of RAPIDS is in the enablement... Read more
Latest Developments in GANs
Generative adversarial networks (GANs) is a compelling technology that’s widely considered one of the most interesting developments in AI and deep learning in the past decade. This article provides an overview of the ODSC West 2018 talk “Latest Developments in GANs,” presented by Seth Weidman of Facebook. The presentation... Read more
Model Evaluation in the Land of Deep Learning
Applications for machine learning and deep learning have become increasingly accessible. For example, Keras provides APIs with TensorFlow backend that enable users to build neural networks without being fluent with TensorFlow. Despite the ease of building and testing models, deep learning has suffered from a lack of interpretability; deep... Read more
Deep Learning in R with Keras
The primary professional hat I wear is as a data science consultant working with machine learning in a variety of problem domains. Due to my academic past in computer science and applied statistics, my development environment of choice today is typically R. Lately however, Python is taking the lead... Read more
Zero to Deep Learning: The Logistics
Deep learning models can be intimidating and rightfully so; in their raw form they are highly complex algorithms that need to be engineered with expertise. However, deep learning is very accessible to individuals with a background in technical skills thanks to organizations and individuals that have packaged deep learning... Read more
Cracking the Box: Interpreting Black Box Machine Learning Models
Intro To kick off this article, I’d like to explain the interpretability of a machine learning (ML) model. According to Merriam-Webster, interpretability describes the process of making something plain or understandable. In the context of ML, interpretability provides us with an understandable explanation of how a model behaves. Basically,... Read more
Smart Image Analysis for E-Commerce Applications
Editor’s note: Abon is a speaker for ODSC West this Fall! Consider attending his talk, “Computer Vision for E-Commerce: Intelligent Analysis and Selection of Product Images at Scale” then. In e-commerce, the role of product images is critical in delivering satisfactory customer experience. Images help online shoppers gain confidence... Read more