Tips and tricks to tune hyperparameters in machine learning that help improve model accuracy

Beginner’s guide to using autoregressive models for forecasting with python

A comprehensive overview of early, deep learning-based approaches for learning tasks on video.

Creating an effective classifier relies on extracting useful features from the underlying data. In this tutorial, we outline a tool that…

Know what you’re REALLY getting into when you train a NER model for Spanish, English, etc. using spaCy

Use gridspec to layout your figure without the need to export multiple plots and use another program to put them together

What I’ve learned as a practitioner of data science

AI systems that can reason across audio, text, and other forms of data are fast becoming a reality. But they have major flaws, like bias.

The integration of AI into devices will improve the efficiency, security, and speed of data analytics. Edge AI is the future of data.

Would you trust AI that has been trained on synthetic data, as opposed to real-world data? You may not know it, but you probably already do.

Posted in r/bigdata by u/Big_Data_Path • 1 point and 0 comments

Write Clean Python Code Using Pipes; 5 Key Skills Needed To Become a Great Data Scientist; A Full End-to-End Deployment of a Machine Learning Algorithm into a Live Production Environment; The 5 Characteristics of a Successful Data Scientist; Top Resources…

What is going on when machine learning can't make the jump from testing to production, and so doesn't add any business value?

From interview through task definition to evaluation

Building Chatbots with Rasa — part III

A gentle deep-dive into the BERT family…

Coding the inner workings with just Python and NumPy

A review of three popular data warehousing techniques

And 10 lessons I learned along the way

A short survey on implicit data augmentation techniques and future research directions