Machine Learning

How to Reduce Variance in the Final Deep Learning Model With a Horizontal Voting Ensemble

Ensemble learning is key in effective machine learning. However, applying ensemble learning to deep models can result in computational challenges due to the depth of the models and the number of models that have to be trained. Horizontal and vertical voting ensembles is a technique that makes applying ensemble learning feasible for deep learning. The… Continue reading How to Reduce Variance in the Final Deep Learning Model With a Horizontal Voting Ensemble

Machine Learning

Ensemble Methods – Abolfazl Ravanshad – Medium

Ensemble Learning can be a lifesaver for many machine learning engineers. It helps to tackle harder problems, simple to train, and even sometimes outperform deep learning. This blog explains different ensemble techniques such as averaging, stacking, bagging and boosting. It also delves into the pros and cons of each of them. In this post, I… Continue reading Ensemble Methods – Abolfazl Ravanshad – Medium

Machine Learning

What’s coming in TensorFlow 2.0 – TensorFlow – Medium

TensorFlow is here to stay on top of the ML & DL libraries, even though very little attention was paid to backward compatibility and abundance of syntactic jargon, but it’s still one of the most efficient and competent libraries to watch for in 2019. TF for both mobile and web are a great addition to… Continue reading What’s coming in TensorFlow 2.0 – TensorFlow – Medium

Machine Learning

Intro to Modern Bayesian Learning and Probabilistic Programming

Bayesian Inference although one of the simples classic techniques in machine learning, it always maintains its position at the top of the list of the most interesting, controversial, and beneficial machine learning algorithms. Combining Bayesian Inference with Probabilistic Modeling and Big Data is a novel trend in open research areas of modern machine learning. Many… Continue reading Intro to Modern Bayesian Learning and Probabilistic Programming