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All You Need to Know about Gradient Boosting Algorithm − Part 1: Regression
Gradient boosting is one of the most popular machine learning algorithms for tabular datasets. It is powerful enough to find any nonlinear relationship between your model target and features and has great usability that can deal with missing values, outliers, and high cardinality categorical values on... Read more
Ivy – The Unified Machine Learning Framework
At Ivy, we’re on a mission to unify all ML frameworks 💥 + automate code conversions 🔄 pip install ivy-core 🚀, join our growing community 😊, and lets-unify.ai! 🦾 In this post, we introduce Ivy, a new ML framework that currently supports JAX, TensorFlow, PyTorch, MXNet, and... Read more
Top 12 Open Source Machine Learning Projects of 2022 (so far)
2022 is rapidly progressing so it’s a good time to take stock of what’s trending in open source machine learning and data science projects. These projects showcase the growth in the field of AI and highlight the current industry trajectory. Using GitHub stars, we tracked the... Read more
7 Reinforcement Learning Use Cases in 2022
Data science and artificial intelligence are everywhere. So are video games. It’s no surprise that it was only a matter of time until people started getting creative with combining the two in unique ways. And no, I’m not talking about improving in-game AI (because clearly, Skyrim... Read more
A Solution for Monitoring Image Data
Editor’s note: Ray Reed is a speaker for ODSC APAC 2022 this September 7th-8th. Be sure to check out his talk, “Monitoring CV Systems: A Unique Solution to a Unique Problem,” there to learn more about monitoring image data. As machine learning ecosystems become increasingly complex... Read more
7 Applications of Auto-Encoders Every Data Scientist Should Know
Auto-Encoders are a popular type of unsupervised artificial neural network that takes unlabeled data and learns efficient codings about the structure of the data that can be used for another context. Auto-Encoders approximate the function that maps the data from full input space to lower dimension... Read more
Staying Ahead of Drift in Machine Learning Systems
Article originally posted here on the iMerit blog. Reposted with permission. Like the rest of us, machine learning systems live in a changing environment. An ML system trained up and ready to go may give great results when initially deployed, but its performance can degrade over... Read more
Essential Guide to Machine Learning Model Monitoring in Production
Model Monitoring is an important component of the end-to-end data science model development pipeline. The robustness of the model not only depends upon the training of the feature engineered data but also depends on how well the model is monitored after deployment. Typically a machine learning... Read more
K Nearest Neighbors From Scratch With Python
K Nearest Neighbors is one of the simplest machine learning algorithms to implement. Its classification for a new instance is based on the target labels of K nearest instances, where K is a tunable hyperparameter. Not only that, but K is the only mandatory hyperparameter. Changing... Read more
Why Accuracy Isn’t Everything: Precision and Recall Simply Explained
A common question in data science interviews is “How would you measure the performance of a classification model when 99% of your data belongs to one class?” This is a straightforward question, yet many people stumble and don’t know how to respond. In this article, we... Read more