The field of natural language processing is shifting from statistical methods to neural network methods. There are still many challenging problems to solve in natural language. Nevertheless, deep learning methods are achieving state-of-the-art results on some specific language problems. It is not just the performance of deep learning models on benchmark problems that is most […]
Search results for "language model"
Oxford Course on Deep Learning for Natural Language Processing
Deep Learning methods achieve state-of-the-art results on a suite of natural language processing problems What makes this exciting is that single models are trained end-to-end, replacing a suite of specialized statistical models. The University of Oxford in the UK teaches a course on Deep Learning for Natural Language Processing and much of the materials for […]
Review of Stanford Course on Deep Learning for Natural Language Processing
Natural Language Processing, or NLP, is a subfield of machine learning concerned with understanding speech and text data. Statistical methods and statistical machine learning dominate the field and more recently deep learning methods have proven very effective in challenging NLP problems like speech recognition and text translation. In this post, you will discover the Stanford […]
Top Books on Natural Language Processing
Natural Language Processing, or NLP for short, is the study of computational methods for working with speech and text data. The field is dominated by the statistical paradigm and machine learning methods are used for developing predictive models. In this post, you will discover the top books that you can read to get started with […]
How to Train a Final Machine Learning Model
The machine learning model that we use to make predictions on new data is called the final model. There can be confusion in applied machine learning about how to train a final model. This error is seen with beginners to the field who ask questions such as: How do I predict with cross validation? Which […]
Deploy Your Predictive Model To Production
5 Best Practices For Operationalizing Machine Learning. Not all predictive models are at Google-scale. Sometimes you develop a small predictive model that you want to put in your software. I recently received this reader question: Actually, there is a part that is missing in my knowledge about machine learning. All tutorials give you the steps up […]
Save And Finalize Your Machine Learning Model in R
Finding an accurate machine learning is not the end of the project. In this post you will discover how to finalize your machine learning model in R including: making predictions on unseen data, re-building the model from scratch and saving your model for later use. Let’s get started. Finalize Your Machine Learning Model Once you have an accurate model […]
Gentle Introduction to Predictive Modeling
When you’re an absolute beginner it can be very confusing. Frustratingly so. Even ideas that seem so simple in retrospect are alien when you first encounter them. There’s a whole new language to learn. I recently received this question: So using the iris exercise as an example if I were to pluck a flower from my […]
Best Programming Language for Machine Learning
A question I get asked a lot is: What is the best programming language for machine learning? I’ve replied to this question many times now it’s about time to explore this further in a blog post. Ultimately, the programming language you use for machine learning should consider your own requirements and predilections. No one can meaningfully address […]
Using LoRA in Stable Diffusion
The deep learning model of Stable Diffusion is huge. The weight file is multiple GB large. Retraining the model means to update a lot of weights and that is a lot of work. Sometimes we must modify the Stable Diffusion model, for example, to define a new interpretation of prompts or make the model to […]