training

How to Train to the Test Set in Machine Learning

Training to the test set is a type of overfitting where a model is prepared that intentionally achieves good performance…

dataset, test, training

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…

cores, model, training

Best 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…

data, decentralized, training

How 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…

data, dataset, training

Special 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…

data, report, training

4 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…

dataset, outliers, training

Stop 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…

data, models, training

Manage 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…

device, model, training

Shrink 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…

gpu-accelerated, training, xgboost

Hand 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…

data, labeling, training

How 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,…

runs, tracking, training

The 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…

building, model, training

Underfitting 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…

student, test, training

Data 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…

learning, model, training

Dipam Vasani

How to be fancy with PythonPython tricks that will make your life easierCustomize your training loop with callbacksLearn how to incorporate state…

training, training looplearn, using fastailearn

How 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…

input, model, training

Big 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…

language, processing, training

Training a Machine Learning Engineer

Once a clear understanding of the problem is established, design the architecture based on the theory youve learnt. I would…

based, learning, training

insideBIGDATA 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…

faster, systems, training

6 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…

data, strategy, training

Types 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…

bias, data, training

MLflow, 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…

function, tracking, training

How (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…

learning, model, training

Predicting 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…

data, points, training

A 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…

data, machine, training