Customizable models: Supports various methods like linear, time decay, or algorithmic models. The cons Complex implementation: Requires advanced tracking and integration across channels.

Customizable models: Supports various methods like linear, time decay, or algorithmic models. The cons Complex implementation: Requires advanced tracking and integration across channels.

Machine Learning Specialization. This specialization, created in collaboration with Stanford Online and DeepLearning.AI, is a three-course program covering supervised learning (linear regression ...

The ability to customize open source models to your specific use case is also a key advantage, and fine-tuned open source models can now outperform GPT-4 on specific tasks, while also providing ...

Executive interview: Open models pros and cons. ... “There’s an opportunity to generate many tokens eventually in a structured way, not just in a linear way,” she says.