Let me know in the comments below. First, we will take a closer look at three main types of learning…
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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,…
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Data Science for Managers: Programming LanguagesTop 8 Data Science Use Cases in SupportActually the unlocking of hidden benefits and true potential…
Continue Reading9 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 ReadingWhy 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 ReadingScaling 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 ReadingA 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 ReadingA 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 ReadingBest 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 ReadingInformation 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 ReadingMathematics 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 ReadingChoosing 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 Reading8 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 ReadingTurning 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 ReadingDeployed 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 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 ReadingA 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 ReadingProbability 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 ReadingData Mapping Using Machine Learning
From small to large businesses, just about every company is fighting for a chance to get their audiences attention.…
Continue ReadingContinuous 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 Reading5 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 ReadingDiscrete 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 ReadingBest 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 ReadingProductionizing 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 ReadingHPE 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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