Autoencoders and Regularization - Learn and Implement

Autoencoders and Regularization – Learn and Implement

In this course, you’ll explore how Autoencoders compress, denoise, and derive valuable features from data. You’ll also delve into regularization to curb overfitting and boost model generalizability. Autoencoders play roles in image enhancement, anomaly spotting, recommendation engines, and generative modelling. Meanwhile, regularization is essential in nearly every machine-learning endeavor. This training will equip you to address practical challenges across various applications.

In today’s data-driven world, professionals with expertise in data preprocessing and model optimization are in high demand. Autoencoders are a powerful tool for data compression and feature extraction. In an era where data is abundant but often noisy or high-dimensional, knowing how to effectively process and represent data is crucial. In addition, overfitting is a common challenge in machine learning. Regularization techniques are your shield against this problem. By learning how to apply regularization effectively, you’ll be able to build models that generalize well to new, unseen data.

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Course Syllabus
Topics that will be covered in the course
Regularization
Autoencoders
Variational Autoencoders
Recommended Skills Prior to Taking this Course
A good understanding of Keras, Linear Regression and Classification and Neural Networks Principles in addition to Python programming language.