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Data Science & Engineering

A Comprehensive Introduction to Bayesian Deep Learning

April 8, 2021 by Joris Baan

Photo by Cody Hiscox on UnsplashPreamble Neural Network Generalization Back to Basics: The Bayesian Approach Frequentists Bayesianists Bayesian Inference and Marginalization How to Use a Posterior in Practice? Maximum A Posteriori Estimation Full Predictive Distribution Approximate Predictive Distribution Bayesian Deep Learning Recent Approaches to Bayesian Deep … [Read more...] about A Comprehensive Introduction to Bayesian Deep Learning

Graph Transformer: A Generalization of Transformers to Graphs

March 23, 2021 by Vijay Prakash Dwivedi

graph transformer

This blog is based on the paper A Generalization of Transformer Networks to Graphs with Xavier Bresson at 2021 AAAI Workshop on Deep Learning on Graphs: Methods and Applications (DLG-AAAI’21).We present Graph Transformer, a transformer neural network that can operate on arbitrary graphs.BLOG OUTLINE:BackgroundObjectiveKey Design Aspects for … [Read more...] about Graph Transformer: A Generalization of Transformers to Graphs

Autoencoders: Overview of Research and Applications

March 18, 2021 by Branislav Holländer

autoencoders

Since the early days of machine learning, it has been attempted to learn good representations of data in an unsupervised manner. The hypothesis underlying this effort is that disentangled representations translate well to downstream supervised tasks. For example, if a human is told that a Tesla is a car and he has a good representation of what a car looks like, he can probably … [Read more...] about Autoencoders: Overview of Research and Applications

Variational Methods in Deep Learning

March 17, 2021 by Branislav Holländer

probabilistic programming

Deep neural networks are a flexible family of models wide applications in AI and other fields. Even though these networks often encompass millions or even billions of parameters, it is still possible to train them effectively using the maximum likelihood principle as well as stochastic gradient descent techniques. Unfortunately, this learning procedure only gives us a … [Read more...] about Variational Methods in Deep Learning

Time Series Forecasting with Deep Learning and Attention Mechanism

February 4, 2021 by Marco Del Pra

time series with deep learning

This is an overview of the architecture and the implementation details of the most important Deep Learning algorithms for Time Series Forecasting.This article was originally published on Towards Data Science and re-published to TOPBOTS with permission from the author.MotivationTime Series Forecasting has always been a very important area of research in many domains … [Read more...] about Time Series Forecasting with Deep Learning and Attention Mechanism

Time Series Classification with Deep Learning

February 2, 2021 by Marco Del Pra

time series with deep learning

This is an overview of the architecture and the implementation details of the most important Deep Learning algorithms for Time Series Classification.This article was originally published on Towards Data Science and re-published to TOPBOTS with permission from the author.Why Time Series Classification?First of all it’s important to underline why this … [Read more...] about Time Series Classification with Deep Learning

Transfer Learning for Time Series Forecasting and Classification

January 12, 2021 by Isaac Godfried

time series forecasting

A brief history: ImageNet was first published in 2009 and over the next four years would go on to form the bedrock of most computer vision models. To this day whether you are training a model to detect pneumonia or classify models of cars you will probably start with a model pre-trained on ImageNet or some other large (and general image) dataset.More recently papers … [Read more...] about Transfer Learning for Time Series Forecasting and Classification

Handling Missing Data For Advanced Machine Learning

June 25, 2020 by Michel Kana

missing data

Throughout this article, you will become good at spotting, understanding, and imputing missing data. We demonstrate various imputation techniques on a real-world logistic regression task using Python. Properly handling missing data has an improving effect on inferences and predictions. This is not to be ignored.The first part of this article presents the framework for … [Read more...] about Handling Missing Data For Advanced Machine Learning

What Do You Need To Do Before Hiring A Data Scientist?

May 12, 2020 by Harpal Singh

hiring data scientist

Are you hiring technical AI talent for your company? Post your openings on the TOPBOTS jobs board (go to jobs board) to reach thousands of engineers, data scientists, and researchers currently looking for work.Artificial Intelligence brings a promise of exponential growth and taking your business to new heights. No wonder there is a lot of excitement around the application … [Read more...] about What Do You Need To Do Before Hiring A Data Scientist?

Top AI Research Advances For Machine Learning Infrastructure

October 24, 2019 by Kate Koidan

ML Infrastructure research

As deep learning models become more and more popular in real-world business applications and training datasets grow very large, machine learning (ML) infrastructure is becoming a critical issue in many companies.To help you stay aware of the latest research advances in ML infrastructure, we’ve summarized some of the most important research papers recently introduced in this … [Read more...] about Top AI Research Advances For Machine Learning Infrastructure

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