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Machine Learning: An In-Depth Guide – Model Performance and Error Analysis
Articles Overview, goals, learning types, and algorithms Data selection, preparation, and modeling Model evaluation, validation, complexity, and improvement Model performance and error analysis Unsupervised learning, related fields, and machine learning in practice Introduction Welcome to the fourth article in a five-part series about machine learning. In... Read more
Recently, I have published an article on Journal of Chemical Physics, entitled Tree based machine learning framework for predicting ground state energies of molecules (link to article and preprint). The article discusses in detail, the application of machine learning algorithms to predict ground state energies of molecules. Current standard of computationally... Read more
Machine Learning: An In-Depth Guide – Model Evaluation, Validation, Complexity, and Improvement
Articles Overview, goals, learning types, and algorithms Data selection, preparation, and modeling Model evaluation, validation, complexity, and improvement Model performance and error analysis Unsupervised learning, related fields, and machine learning in practice Introduction Welcome to the third article in a five-part series about machine learning. In... Read more
Machine Learning: An In-Depth Guide – Data Selection, Preparation, and Modeling
Articles Overview, goals, learning types, and algorithms Data selection, preparation, and modeling Model evaluation, validation, complexity, and improvement Model performance and error analysis Unsupervised learning, related fields, and machine learning in practice Introduction Welcome to the second article in a five-part series about machine learning. In... Read more
Machine Learning: An In-Depth Guide – Overview, Goals, Learning Types, and Algorithms
Articles Overview, goals, learning types, and algorithms Data selection, preparation, and modeling Model evaluation, validation, complexity, and improvement Model performance and error analysis Unsupervised learning, related fields, and machine learning in practice Introduction Welcome! This is the first article of a five-part series about machine learning.... Read more
A survey of cross-lingual embedding models
In past blog posts, we discussed different models, objective functions, and hyperparameter choices that allow us to learn accurate word embeddings. However, these models are generally restricted to capture representations of words in the language they were trained on. The availability of resources, training data, and... Read more
The Complexities of Governing Machine Learning
Today’s businesses run on data. It’s essential for any corporation to look for insights about their customers based on the data they collect. That collected information drives everything from business strategy to customer service. In order to retrieve insights from the massive amounts of data they... Read more
The future of Machine Learning lies in its (human) past
Superficially different in goals and approach, two recent algorithmic advances, Bayesian Program Learning and Galileo, are examples of one of the most interesting and powerful new trends in data analysis. It also happens to be the oldest one. Bayesian Program Learning (BPL) is deservedly one of... Read more
Cognitive Machine Learning (2): Uncertain Thoughts
She pined in thought,  And with a green and yellow melancholy She sat like Patience on a monument,  Smiling at grief. Was not this love indeed?   In King Lear, Shakespeare stirs a sense of self-consciousness by invoking Patience, sitting up high;... Read more
Transfer Learning – Machine Learning’s Next Frontier
Table of contents: What is Transfer Learning? Why Transfer Learning Now? A Definition of Transfer Learning Transfer Learning Scenarios Applications of Transfer Learning Learning from simulations Adapting to new domains Transferring knowledge across languages Transfer Learning Methods Using pre-trained CNN features Learning domain-invariant representations Making representations... Read more