Mastering Neural Networks and Model Regularization
Coursera
24 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
The course "Mastering Neural Networks and Model Regularization" dives deep into the fundamentals and advanced techniques of neural networks, from understanding perceptron-based models to implementing cutting-edge convolutional neural networks (CNNs). This course offers hands-on experience with real-world datasets, such as MNIST, and focuses on practical applications using the PyTorch framework.
Learners will explore key regularization techniques like L1, L2, and drop-out to reduce model overfitting, as well as decision tree pruning. What makes this course unique is its emphasis on building neural networks from scratch, allowing learners to grasp the intricate details of model design and training.
Additionally, the course covers computational graphs, activation and loss functions, and how to efficiently utilize GPUs for faster computation. Learners will also delve into CNNs for image and audio processing, gaining insights into cutting-edge applications in these fields.
By completing this course, learners will develop advanced skills in neural network design, model regularization, and the use of PyTorch for deep learning tasks—empowering them to tackle complex machine learning challenges with confidence.
University: Johns Hopkins University
Provider: Coursera
Categories: Machine Learning Courses, Deep Learning Courses, Neural Networks Courses, PyTorch Courses
