Anomaly Detection (13.2)

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This video introduces Anomaly Detection using Autoencoders in PyTorch. In this session, we'll delve deep into the fascinating world of deep learning, particularly focusing on the powerful technique of autoencoders and their application in identifying anomalies in data. We'll start by introducing the basic concepts of autoencoders, explaining how these neural networks learn to compress and reconstruct data. Then, we'll explore how this capability can be leveraged for anomaly detection, a critical task in various domains like fraud detection, system health monitoring, and more. Throughout the video, we'll be using PyTorch, a leading deep learning library, to build and train our autoencoder model. Whether you're a beginner in machine learning or looking to expand your skills, this tutorial promises to provide valuable insights and practical coding examples to enhance your understanding of anomaly detection using autoencoders in PyTorch.

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#reinforcementlearning #gymnasium #PyTorch #DeepLearning #PyTorchTutorial #MachineLearning

Jeff Heaton

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Watching your videos (old) somewhere 5 years ago, now I am having a few papers on it. Thank you for providing an interesting walk- around....🎉

Keep it up 💪

datalabwork

Hi, thanks for this video and all the resources to try and run it yourself!
BUT: It is not purely unsupervised anomaly detection, since we know the anomalies (that's how we created the data sets, 'normal' vs. anything else) and use them for the evaluation. This makes it a “one-class classification problem” rather than a true anomaly detection. The problem lies somehow in between.

kvg_

this may not be the video for it but can you please please help me out
I am using windows 11

Nvidia drivers is 555.99 which supports CUDA 12.5
but Cuda 12.5 does not support TensorFlow 2.10 and im stuck in this infinte loop of fixing one thing after another

farazfitness

Anomaly Detection (13.2)

Anomaly Detection (13.2)

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