Introduction to Aggregate and Transform functions in Apache Spark
Spark SQL functions, such as the aggregate and transform can be used instead of UDFs to manipulate complex array data containing nested structures.
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Category: Python
Python tutorials
CI/CD for ML Projects
CI: Continuous Integration CD: Continuous Deployment/Delivery
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Building a GPT-3 Powered Discord Support Bot
Discord is becoming a popular platform for open source projects and companies to manage their developer communities. These Discord servers are where developers can go to get their questions answered quickly. Some support channels are extremely busy with the same questions being asked and answered over and over again. I figured that answering these questions might be something that GPT-3 could do really well! So I decided to spend a couple hours to build the bot — this article talks […]
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The importance of multilingual corpora in the democratization of NLP
Is a universal translator the end goal?
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Introducing Natural Language Processing (NLP) Service for Pharmacovigilance (PV)
Pharmacovigilance (PV) is the process of collecting, detecting, assessing, monitoring, and preventing Adverse Events (AEs) of pharmaceutical products that ensure product safety. PV is a critical area in which language technology can significantly add value. Due to the increasing occurrences of mentioning drug side effects on social media, the data has become a crucial source of public information for evaluating the consequence and efficiency of the medicine.
Read moreRefactoring: Prepare Your Code to Get Help
At some point in your Python journey, you’ll look to other programmers for help with a sticking point in your code. When that day comes, it’s important to make sure that you’re asking a clear question and that the code is accessible and executable. If you can make it easier for people to help you out, then you’re more likely to get the answers that you need. In this video course, you’ll learn how to: Write a clear, concise question […]
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Optimizing heavy models with early exit branches
Everyday models get heavier and heavier (in terms of learnable parameters). For example, LEMON_large has 200M parameters and GPT-3 has over 175 billion parameters!
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