Bayesian Machine Learning (Part 8)
Bayesian Machine Learning (part-8) Mean Field Approximation Have you ever asked a question, why do we need to calculate the exact Posterior distribution ? To… Read More »Bayesian Machine Learning (Part 8)
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Machine Learning
Knowledge graphs are network graphs that link related concepts and properties together to create a form of inferencing engine, with knowledge engineering being the programming aspect of graph usage. Explore how knowledge graphs are created and queried, how they are used as part of a broader form of enterprise metadata management, and how they tie into ML and the IoT.
New Algorithm For Density Estimation and Noise Reduction
KernelML – Hierarchical Density Factorization The purpose, problem statement, and potential applications came from this post on datasciencecentral.com. The goal is to approximate any multi-variate distribution using… Read More »New Algorithm For Density Estimation and Noise Reduction
Speedup by 10x the Hyperparameter tuning of ML applications on Kubeflow using FPGAs
Kubernetes is a great system for handling clusters of containers (whether on cloud or on-premise), but deploying and managing containerized applications for ML can be… Read More »Speedup by 10x the Hyperparameter tuning of ML applications on Kubeflow using FPGAs
Limits of linear models for forecasting
This article was written by Blaine Bateman. In this post, I will demonstrate the use of nonlinear models for time series analysis, and contrast to linear… Read More »Limits of linear models for forecasting
Compressing information through the information bottleneck during deep learning
This article was written by Ray. Read an article in Quanta Magazine (New theory cracks open the black box of deep learning) about a talk (see… Read More »Compressing information through the information bottleneck during deep learning
How you can explain Machine Learning models ?
Machine Learning (ML) models are increasingly being used to augment human decision making process in domains such as finance, telecommunication, healthcare, and others. In most… Read More »How you can explain Machine Learning models ?
Let's talk about Filters in Computer Vision(with Images), what are they and how they work in easy way.
Filters are the key thing in Computer Vision(Processing image data). You would have probably used different kinds of filters like the blur filter, vintage filters,… Read More »Let's talk about Filters in Computer Vision(with Images), what are they and how they work in easy way.
Run 100x faster your Scikit-learn ML apps: A use case on Naive Bayes
Scikit-learn (also known as sklearn) is a widely used free software machine learning library for the Python programming language. It has been adopted by many… Read More »Run 100x faster your Scikit-learn ML apps: A use case on Naive Bayes
Regularization in Machine Learning
This article was written by Prashant Gupta. One of the major aspects of training your machine learning model is avoiding overfitting. The model will have… Read More »Regularization in Machine Learning
How Machine Learning Is Changing the World
Machine learning (ML) is an application of Artificial Intelligence (AI) that provides the system with the ability to automatically learn and improve from experience rather… Read More »How Machine Learning Is Changing the World