摘要： Neural architecture search promises to speed up the process of finding neural network architectures that will yield good models for a given dataset.
摘要： Last year, San Francisco-based research lab OpenAI released Codex, an AI model for translating natural language commands into app code. The model, which powers GitHub’s Copilot feature, was heralded at the time as one of the most powerful examples of machine programming, the category of tools that automates the development and maintenance of software. Not to be outdone, DeepMind — the AI lab backed by Google parent company Alphabet — claims to have improved upon Codex in key areas with AlphaCode, a system that can write “competition-level” code.
摘要： The enterprise is investing heavily into multiple forms of AI, but interest in natural language processing (NLP) has gained momentum in the past few months.
摘要： Google Cloud Platform (GCP) is offering new dedicated data and machine learning (ML) tools designed to clear up data inefficiencies and ease application development for enterprises.
摘要：
Machine learning is an exciting area of research and development. ML tools are important in many industries and science fields. ML research is also very tricky and has several challenges. If not addressed suitably, these challenges can lead the project in the wrong direction.
摘要： In such cases, FbProphet is your savior; easy, fast and gives good performance. If you know how FbProphet works, you can use it well to fit your time-series data.
摘要： Rendezvous Architecture helps you run and choose outputs from a Champion model and many Challenger models running in parallel without many overheads. The original approach works well for smaller data sets, so how can this idea adapt to big data pipelines?
