#Gradient Descent

This comprehensive guide examines TensorFlow 2, focusing on its user-friendly API and resemblance to NumPy. It covers efficient computation strategies, symbolic and iterative methods, and control flow operations such as conditionals and loops. The guide also addresses debugging when using broadcasting and using overloaded operators for clearer code. In addition, it discusses prototyping with Python operations and advanced visualization. The emphasis is on numerical stability to aid the development of robust AI models, with updates tracking the latest TensorFlow API releases.

The repository contains detailed solutions and practical notes for the Machine Learning Specialization by Andrew Ng on Coursera. It covers essential topics such as Supervised Learning, Regression, and Advanced Algorithms including Neural Networks. The content is suitable for both beginners and advanced learners, featuring optional labs and quizzes to enhance understanding. It also offers resources on Mathematics for Machine Learning and Data Science, providing a solid theoretical foundation. These materials serve as valuable resources on the journey to mastering machine learning.