Last Updated on December 6, 2019Machine learning algorithms are typically evaluated using resampling techniques such as k-fold cross-validation. During the…

Continue Reading# Machine Learning news

## Azure Databricks Highlights Adoption of Delta Lake, MLflow, and Integration with Azure Machine Learning at Microsoft Ignite 2019

It was an action-packed week of making new connections and learning about new innovation across data science, data engineering, and…

Continue Reading## Supercomputers and Machine Learning: A Perfect Match

To understand High-Performance (HPC) you need to first understand supercomputers. A supercomputer is a type of HPC solution which performs…

Continue Reading## What Does Stochastic Mean in Machine Learning?

Last Updated on November 18, 2019The behavior and performance of many machine learning algorithms are referred to as stochastic. Stochastic…

Continue Reading## A Gentle Introduction to Model Selection for Machine Learning

Given easy-to-use machine learning libraries like scikit-learn and Keras, it is straightforward to fit many different machine learning models on…

Continue Reading## How to Choose a Feature Selection Method For Machine Learning

Last Updated on November 28, 2019Feature selection is the process of reducing the number of input variables when developing a…

Continue Reading## 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…

Continue Reading## How to Save a NumPy Array to File for Machine Learning

Last Updated on November 13, 2019Developing machine learning models in Python often requires the use of NumPy arrays. NumPy arrays…

Continue Reading## Want to Build Machine Learning Pipelines? A Quick Introduction using PySpark

Here’s the caveat – Spark’s OneHotEncoder does not directly encode the categorical variable. First, we need to use the String…

Continue Reading## A Unique Method for Machine Learning Interpretability: Game Theory & Shapley Values!

Now, if we talk in terms of Game Theory, the “game” here is the prediction task for a single instance…

Continue Reading## Two Years In The Life of AI, Machine Learning, Deep Learning and Java

Which libraries and frameworks to use? Another confession, I didn’t spend too much time trying to gather and categorise these topics,…

Continue Reading## A Doomed Marriage of Machine Learning and Agile

I will never forget this date. I love success stories about ML. But, let’s face the truth: most ML…

Continue Reading## How I Got Better at Machine Learning

This means that if you know these fundamentals, it will be much easier and less time-consuming for you to learn…

Continue Reading## Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

The paper is a mix of technical and philosophical arguments and comes with two main takeaways for me: firstly, a…

Continue Reading## 14 Types of Learning in Machine Learning

Let me know in the comments below. First, we will take a closer look at three main types of learning…

Continue Reading## A Gentle Introduction to Maximum a Posteriori (MAP) for Machine Learning

Density estimation is the problem of estimating the probability distribution for a sample of observations from a problem domain. Typically,…

Continue Reading## ActiveWizards: machine learning company

Data Science for Managers: Programming LanguagesTop 8 Data Science Use Cases in SupportActually the unlocking of hidden benefits and true potential…

Continue Reading## 9 Practical Actions to Improve Machine Learning for Fraud Prevention

Start with simpler, more transparent, and explainable and bias-free models and graduate to complicated models over time. MODELS – Experiment…

Continue Reading## Why is Machine Learning Deployment Hard?

By Alexandre Gonfalonieri, AI Consultant. After several AI projects, I realized that deploying Machine Learning (ML) models at scale is…

Continue Reading## Scaling Hyperopt to Tune Machine Learning Models in Python

Hyperopt is an open-source hyperparameter tuning library written for Python. With 445,000+ PyPI downloads each month and 3800+ stars…

Continue Reading## A Gentle Introduction to Maximum Likelihood Estimation for Machine Learning

Density estimation is the problem of estimating the probability distribution for a sample of observations from a problem domain. There…

Continue Reading## A Gentle Introduction to Cross-Entropy for Machine Learning

Cross-entropy is commonly used in machine learning as a loss function. Cross-entropy is a measure from the field of information…

Continue Reading## Best of arXiv.org for AI, Machine Learning, and Deep Learning – September 2019

rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch Since the recent advent of deep reinforcement learning for…

Continue Reading## Information Gain and Mutual Information for Machine Learning

Information gain calculates the reduction in entropy or surprise from transforming a dataset in some way. It is commonly used…

Continue Reading## Mathematics behind Machine Learning – The Core Concepts you Need to Know

Well, that’s what we will learn in this article. We’ll discuss the various mathematical aspects you need to know to…

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