[Video tutorial] Improve the Latency for Larger Neural Networks in Concrete ML

Concrete ML is a set of tools designed to facilitate the use of Fully Homomorphic Encryption (FHE) in machine learning by enabling developers to automatically convert machine learning models into their homomorphic equivalents. A tutorial by Zama team member Jordan Frery focuses on improving the latency of larger neural networks within Concrete ML. Users are encouraged to support the project by starring the Concrete ML GitHub repository, reviewing documentation, engaging on community channels, and participating in the Zama Bounty Program to further advance the field of FHE.