#Coursera

Gain a well-rounded understanding of deep learning across five structured courses, featuring practical assignments, quiz solutions, and guidance from Andrew Ng on Coursera. Access a repository rich in resources for mastering neural networks, hyperparameter tuning, and convolutional neural networks, perfect for computer science students and self-learners looking to enhance their machine learning skills and debugging strategies. Build a solid foundation in AI technologies with valuable course content.

This Coursera specialization by Andrew Ng offers an in-depth look into deep learning with neural networks, regularization, convolutional networks, and sequence models. It includes practical programming assignments and quizzes, and has been updated for TensorFlow 2. Suitable for those interested in AI applications in computer vision and NLP, it covers advanced topics like Transformer networks and YOLO. Includes structured guidance and practical case studies.

Discover MLOps workflows enhanced by Rust, offering alternatives to conventional Python-based stacks like Jupyter and Pandas. This repository, styled as a practical guide, provides insights into crafting robust MLOps solutions using Rust to take advantage of its superior performance and energy efficiency. It showcases demos ranging from PyTorch model training to deploying models with Rust frameworks such as Actix. Embrace innovative and high-speed computational methods centered around containerized workflows, with in-depth examples and advanced demonstrations emphasizing infrastructure efficiency.