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The Interplay of Experimentation and ML to Aid in Repayment of Micro-Loans in Sub-Saharan Africa
In this article, we look at the interplay of experimentation and ML to aid in the repayment of micro-loans, specifically in sub-Saharan Africa. Read on to understand the story that brought us to this understanding. Imagine that through a twist of fate,... 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
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
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
Model Interpretation: What and How?
Editor’s note: Brian is a speaker for ODSC West in California this November! Be sure to check out his talk, “Advanced Methods for Explaining XGBoost Models” there! As modern machine learning methods become more ubiquitous, increasing attention is being paid to understanding how these models work — model interpretation instead... Read more
Redefining Robotics: Next Generation Warehouses
People picture robots changing to look more like humans, but in reality, the evolution of robotics involves things you can’t actually see. For Bastiane Huang at Osaro, the development of robots means greater advances in autonomy. Building brains for robots gives them more flexibility for tasks and creates more... Read more
Recognize Class Imbalance with Baselines and Better Metrics
In my first machine learning course as an undergrad, I built a recommender system. Using a dataset from a social music website, I created a model to predict whether a given user would like a given artist. I was thrilled when initial experiments showed that for 99% of the... Read more
NVIDIA GPUs and Apache Spark, One Step Closer
While RAPIDS started with a Python API focus, there are many who want to enjoy the same NVIDIA GPU acceleration in Apache Spark; in fact, we have many at NVIDIA. When RAPIDS first launched, we had a plan to accelerate Apache Spark as well as Dask, and we want to share some major accomplishments we’ve... Read more
Bias Variance Decompositions using XGBoost
This blog dives into a theoretical machine learning concept called the bias-variance decomposition. This decomposition is a method which examines the expected generalization error for a given learning algorithm and a given data source. This helps us understand questions like: – How can I achieve higher accuracy with my... Read more