Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

Overview

Embark on a transformative journey into the realm of AI with the "Improving Deep Neural Networks:

Hyperparameter Tuning, Regularization, and Optimization" course, available on Coursera. This comprehensive course serves as the second installment in the Deep Learning Specialization, designed to demystify the inner workings of deep learning and provide systematic guidelines for achieving superior performance.

Throughout this course, participants will delve into the critical aspects of training deep learning models, including the creation of training and test sets, along with an in-depth analysis of bias/variance to guide the development of sophisticated deep learning applications.

The curriculum covers essential neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking. Additionally, students will explore a variety of optimization algorithms, including mini-batch gradient descent, Momentum, RMSprop, and Adam, ensuring a thorough understanding of their convergence mechanisms.

Another key component of this course is the practical implementation of a neural network using TensorFlow, allowing learners to apply their knowledge in a hands-on environment.

This specialization paves the way for participants to grasp the complexities, challenges, and potential of deep learning, equipping them with the skills necessary to contribute to the advancement of AI technology. Whether you're looking to enhance your expertise in machine learning, elevate your technical career, or make significant strides in the AI landscape, this course offers the perfect opportunity to acquire vital knowledge and skills in deep learning, neural networks, TensorFlow, and hyperparameter optimization.

Categories include:

Deep Learning Courses, Neural Networks Courses, TensorFlow Courses, Hyperparameter Optimization Courses.

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Taught by

Andrew Ng

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Subjects