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

Continue Reading# Machine Learning news

## 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…

Continue Reading## Choosing a Machine Learning Model

For example, if linear regression seemed to work best, it might be a good idea to try lasso or ridge…

Continue Reading## 8 Paths to Getting a Machine Learning Job Interview

But major universities, in particular, can have decent job fairs. However, my real recommendation is that networking events and meetups…

Continue Reading## Turning IT Upside Down In a Machine Learning World

As the legend goes, cows tend to take the path of least resistance from point A to point B and…

Continue Reading## Deployed your Machine Learning Model? Here’s What you Need to Know About Post-Production Monitoring

The next best thing to do is to continuously track the health of the machine learning model against a set…

Continue Reading## 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…

Continue Reading## A Gentle Introduction to Bayes Theorem for Machine Learning

Bayes Theorem provides a principled way for calculating a conditional probability. It is a deceptively simple calculation, although it can…

Continue Reading## Probability for Machine Learning (7-Day Mini-Course)

This is called the “Boy or Girl Problem” and is one of many common toy problems for practicing probability. Post…

Continue Reading## Data Mapping Using Machine Learning

From small to large businesses, just about every company is fighting for a chance to get their audiences attention.…

Continue Reading## Continuous Probability Distributions for Machine Learning

The probability for a continuous random variable can be summarized with a continuous probability distribution. Continuous probability distributions are encountered…

Continue Reading## 5 Beginner Friendly Steps to Learn Machine Learning and Data Science with Python

I put together a couple of steps in the reply and I’m copying them here. You can consider them a…

Continue Reading## Discrete Probability Distributions for Machine Learning

The probability for a discrete random variable can be summarized with a discrete probability distribution. Discrete probability distributions are used…

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

A Probabilistic Representation of Deep Learning This paper introduces a novel probabilistic representation of deep learning, which provides an explicit…

Continue Reading## Productionizing Machine Learning: From Deployment to Drift Detection

Try this notebook to reproduce the steps outlined below and watch our on-demand webinar to learn more. In many articles and…

Continue Reading## HPE Accelerates Artificial Intelligence Innovation with Enterprise-grade Solution for Managing Entire Machine Learning Lifecycle

Hewlett Packard Enterprise (HPE) announced a container-based software solution, HPE ML Ops, to support the entire machine learning model lifecycle…

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