Imagine you trained a machine learning model. Maybe, a couple of candidates to choose from.You ran them on the test set and got some quality estimates. Models are not overfitted. Features make sense. Overall, they perform as well as they can, given the limited data at hand.Now, it is time to decide if any of them is good enough for production use. How to evaluate … [Read more...] about What Is Your Model Hiding? A Tutorial on Evaluating ML Models
Data Science & Engineering
A Comprehensive Introduction to Bayesian Deep Learning
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
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
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
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
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
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
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
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?
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?