Best And Worst Cases Of Machine Learning Algo
What are the best and worst cases where machine learning algorithms can be applied? Can anyone help me in this?
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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.
Top Machine Learning Tools for Easy Development
Let’s start with some simple real-lie examples that we’re sure you all must have experienced. You watch Netflix and it offers you viewing suggestions. Twitter… Read More »Top Machine Learning Tools for Easy Development
Automatic Differentiation in 15 Minutes — video tutorial with application in machine learning and finance
Recorded in Bloomberg’s London offices in November 2019: slides here
Model Transparency and the Complication of Model-Based vs Model-Free Learning
Summary: High stakes models like those that allocate scarce resources to competing hospitals are headline news. New thinking contrasting model-based versus model-free learning are emerging… Read More »Model Transparency and the Complication of Model-Based vs Model-Free Learning
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)
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 ?