Training to the test set is a type of overfitting where a model is prepared that intentionally achieves good performance…
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Multi-Core Machine Learning in Python With Scikit-Learn
Many computationally expensive tasks for machine learning can be made parallel by splitting the work across multiple CPU cores, referred…
Continue ReadingBest of arXiv.org for AI, Machine Learning, and Deep Learning – August 2020
In this recurring monthly feature, we filter recent research papers appearing on the arXiv. org preprint server for compelling subjects…
Continue ReadingHow to Treat Overfitting in Convolutional Neural Networks
Introduction Overfitting or high variance in machine learning models occurs when the accuracy of your training dataset, the dataset used…
Continue ReadingSpecial Report: The State of AI and Machine Learning
Appen Limited, a leading provider of high-quality training data for organizations that build effective AI systems at scale, released its…
Continue Reading4 Automatic Outlier Detection Algorithms in Python
The presence of outliers in a classification or regression dataset can result in a poor fit and lower predictive modeling…
Continue ReadingStop training more models, start deploying them
By Maurits Kaptein, Tilburg University The rumours that AI (and ML) will revolutionise healthcare have been around for a while [1]. And yes, we…
Continue ReadingManage and Scale Machine Learning Models for IoT Devices
A common data science internet of things (IoT) use case involves training machine learning models on real-time data coming from…
Continue ReadingShrink Training Time and Cost Using NVIDIA GPU-Accelerated XGBoost and Apache Spark™ on Databricks
Guest Blog by Niranjan Nataraja and Karthikeyan Rajendran of Nvidia. Niranjan Nataraja is a lead data scientist at Nvidia and…
Continue ReadingHand labeling is the past. The future is #NoLabel AI
By Russell Jurney, Consultant and machine learning engineer. We are witnessing a data labeling market explosion: labeling platforms have hit…
Continue ReadingHow to Display Model Metrics in Dashboards using the MLflow Search API
Machine learning engineers and data scientists frequently train models to optimize a loss function. With optimization methods like gradient descent,…
Continue ReadingThe Future of Machine Learning Will Include a Lot Less Engineering
By David LiCause, Data Scientist Building a useful machine learning product involves creating a multitude of engineering components, only a small…
Continue ReadingUnderfitting vs. Overfitting (vs. Best Fitting) in Machine Learning
The Challenge of Underfitting and Overfitting in Machine Learning You’ll inevitably face this question in a data scientist interview: Can…
Continue ReadingData Validation for Machine Learning
Data is the sustenance that keeps machine learning going. No matter how powerful a machine learning and/or deep learning model…
Continue ReadingDipam Vasani
How to be fancy with PythonPython tricks that will make your life easierCustomize your training loop with callbacksLearn how to incorporate state…
Continue ReadingHow to Connect Model Input Data With Predictions for Machine Learning
Fitting a model to a training dataset is so easy today with libraries like scikit-learn. A model can be fit…
Continue ReadingBig Models are Carbon Efficient if you Share Them
Recently, a research team at The University of Massachusetts Amherst led by Emma Strubell published a paper on the carbon…
Continue ReadingTraining a Machine Learning Engineer
Once a clear understanding of the problem is established, design the architecture based on the theory youve learnt. I would…
Continue ReadinginsideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads – Part 3
Artificial Intelligence (AI) and Deep Learning (DL) represent some of the most demanding workloads in modern computing history as they…
Continue Reading6 Tips for Building a Training Data Strategy for Machine Learning
By Wilson Pang, CTO, Appen. Artificial intelligence (AI) and machine learning (ML) are frequently used terms these days. AI refers…
Continue ReadingTypes of Bias in Machine Learning
In one my previous posts I talke about the biases that are to be expected in machine learning and can…
Continue ReadingMLflow, TensorFlow, and an Open Source Show
This summer, I interned on the ML Platform team. I worked on MLflow, an open-source machine learning management framework. This…
Continue ReadingHow (Not) To Scale Deep Learning in 6 Easy Steps
However, there’s an important flaw. The final evaluation on the held-out 10% validation data shows that true accuracy is more…
Continue ReadingPredicting environmental carcinogens with logistic regression, knn, gradient boosting and molecular fingerprinting
Predicting environmental carcinogens with logistic regression, knn, gradient boosting and molecular fingerprintingBalancing imbalanced data, exploring accuracy metrics, and an introduction…
Continue ReadingA Brief History of Training Data
A Brief History of Training DataRobert MunroBlockedUnblockFollowFollowingJul 3A old friend, Aman Naimat, recently hosted a conference that brought together people with…
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