Foundations of Neural Networks
Coursera
31 Courses
Johns Hopkins University is a globally recognized research university comprising 9 schools and campuses worldwide. It provides more than 260 degree programs, ranging from undergraduate to graduate and postdoctoral studies.
Overview
This Specialization by Johns Hopkins University, available on Coursera, is tailored for post-graduate students aiming to advance their expertise in neural networks and deep learning. The program encompasses three comprehensive courses that delve into the mathematical theories of neural networks, exploring architectures such as feed-forward, convolutional, and recurrent neural networks.
You will gain insight into deep learning optimization, regularization techniques, unsupervised learning, and the intriguing world of generative adversarial networks. Additionally, the specialization addresses the ethical challenges inherent in applying neural network technologies, equipping you to manage these considerations effectively.
Upon completion, you will possess practical experience in creating and implementing algorithms using Python, enabling the application of theoretical insights to real-world data challenges. The program prepares you to design, analyze, and operationalize neural networks for practical applications across AI, machine learning, and data science domains.
By progressing through the curriculum, you'll attain the skills to independently implement and assess various neural network models, establishing a robust foundation for a career in AI research or development.
University: Johns Hopkins University
Provider: Coursera
Categories: Artificial Intelligence Courses, Python Courses, Machine Learning Courses, Deep Learning Courses, Neural Networks Courses
